Monday, 30 September 2013

Web Scraper Shortcode WordPress Plugin Review

This short post is on the WP-plugin called Web Scraper Shortcode, that enables one to retrieve a portion of a web page or a whole page and insert it directly into a post. This plugin might be used for getting fresh data or images from web pages for your WordPress driven page without even visiting it. More scraping plugins and sowtware you can find in here.

To install it in WordPress go to Plugins -> Add New.
Usage

The plugin scrapes the page content and applies parameters to this scraped page if specified. To use the plugin just insert the

[web-scraper ]

shortcode into the HTML view of the WordPress page where you want to display the excerpts of a page or the whole page. The parameters are as follows:

    url (self explanatory)
    element – the dom navigation element notation, similar to XPath.
    limit – the maximum number of elements to be scraped and inserted if the element notation points to several of them (like elements of the same class).

The use of the plugin is of the dom (Data Object Model) notation, where consecutive dom nodes are stated like node1.node2; for example: element = ‘div.img’. The specific element scrape goes thru ‘#notation’. Example: if you want to scrape several ‘div’ elements of the class ‘red’ (<div class=’red’>…<div>), you need to specify the element attribute this way: element = ‘div#red’.
How to find DOM notation?

But for inexperienced users, how is it possible to find the dom notation of the desired element(s) from the web page? Web Developer Tools are a handy means for this. I would refer you to this paragraph on how to invoke Web Developer Tools in the browser (Google Chrome) and select a single page element to inspect it. As you select it with the ‘loupe’ tool, on the bottom line you’ll see the blue box with the element’s dom notation:


The plugin content

As one who works with web scraping, I was curious about  the means that the plugin uses for scraping. As I looked at the plugin code, it turned out that the plugin acquires a web page through ‘simple_html_dom‘ class:

    require_once(‘simple_html_dom.php’);
    $html = file_get_html($url);
    then the code performs iterations over the designated elements with the set limit

Pitfalls

    Be careful if you put two or more [web-scraper] shortcodes on your website, since downloading other pages will drastically slow the page load speed. Even if you want only a small element, the PHP engine first loads the whole page and then iterates over its elements.
    You need to remember that many pictures on the web are indicated by shortened URLs. So when such an image gets extracted it might be visible to you in this way: , since the URL is shortened and the plugin does not take note of  its base URL.
    The error “Fatal error: Call to a member function find() on a non-object …” will occur if you put this shortcode in a text-overloaded post.

Summary

I’d recommend using this plugin for short posts to be added with other posts’ elements. The use of this plugin is limited though.



Source: http://extract-web-data.com/web-scraper-shortcode-wordpress-plugin-review/

Friday, 27 September 2013

Visual Web Ripper: Using External Input Data Sources

Sometimes it is necessary to use external data sources to provide parameters for the scraping process. For example, you have a database with a bunch of ASINs and you need to scrape all product information for each one of them. As far as Visual Web Ripper is concerned, an input data source can be used to provide a list of input values to a data extraction project. A data extraction project will be run once for each row of input values.

An input data source is normally used in one of these scenarios:

    To provide a list of input values for a web form
    To provide a list of start URLs
    To provide input values for Fixed Value elements
    To provide input values for scripts

Visual Web Ripper supports the following input data sources:

    SQL Server Database
    MySQL Database
    OleDB Database
    CSV File
    Script (A script can be used to provide data from almost any data source)

To see it in action you can download a sample project that uses an input CSV file with Amazon ASIN codes to generate Amazon start URLs and extract some product data. Place both the project file and the input CSV file in the default Visual Web Ripper project folder (My Documents\Visual Web Ripper\Projects).

For further information please look at the manual topic, explaining how to use an input data source to generate start URLs.


Source: http://extract-web-data.com/visual-web-ripper-using-external-input-data-sources/

Thursday, 26 September 2013

Using External Input Data in Off-the-shelf Web Scrapers

There is a question I’ve wanted to shed some light upon for a long time already: “What if I need to scrape several URL’s based on data in some external database?“.

For example, recently one of our visitors asked a very good question (thanks, Ed):

    “I have a large list of amazon.com asin. I would like to scrape 10 or so fields for each asin. Is there any web scraping software available that can read each asin from a database and form the destination url to be scraped like http://www.amazon.com/gp/product/{asin} and scrape the data?”

This question impelled me to investigate this matter. I contacted several web scraper developers, and they kindly provided me with detailed answers that allowed me to bring the following summary to your attention:
Visual Web Ripper

An input data source can be used to provide a list of input values to a data extraction project. A data extraction project will be run once for each row of input values. You can find the additional information here.
Web Content Extractor

You can use the -at”filename” command line option to add new URLs from TXT or CSV file:

    WCExtractor.exe projectfile -at”filename” -s

projectfile: the file name of the project (*.wcepr) to open.
filename – the file name of the CSV or TXT file that contains URLs separated by newlines.
-s – starts the extraction process

You can find some options and examples here.
Mozenda

Since Mozenda is cloud-based, the external data needs to be loaded up into the user’s Mozenda account. That data can then be easily used as part of the data extracting process. You can construct URLs, search for strings that match your inputs, or carry through several data fields from an input collection and add data to it as part of your output. The easiest way to get input data from an external source is to use the API to populate data into a Mozenda collection (in the user’s account). You can also input data in the Mozenda web console by importing a .csv file or importing one through our agent building tool.

Once the data is loaded into the cloud, you simply initiate building a Mozenda web agent and refer to that Data list. By using the Load page action and the variable from the inputs, you can construct a URL like http://www.amazon.com/gp/product/%asin%.
Helium Scraper

Here is a video showing how to do this with Helium Scraper:


The video shows how to use the input data as URLs and as search terms. There are many other ways you could use this data, way too many to fit in a video. Also, if you know SQL, you could run a query to get the data directly from an external MS Access database like
SELECT * FROM [MyTable] IN "C:\MyDatabase.mdb"

Note that the database needs to be a “.mdb” file.
WebSundew Data Extractor
Basically this allows using input data from external data sources. This may be CSV, Excel file or a Database (MySQL, MSSQL, etc). Here you can see how to do this in the case of an external file, but you can do it with a database in a similar way (you just need to write an SQL script that returns the necessary data).
In addition to passing URLs from the external sources you can pass other input parameters as well (input fields, for example).
Screen Scraper

Screen Scraper is really designed to be interoperable with all sorts of databases. We have composed a separate article where you can find a tutorial and a sample project about scraping Amazon products based on a list of their ASINs.


Source: http://extract-web-data.com/using-external-input-data-in-off-the-shelf-web-scrapers/

Tuesday, 24 September 2013

Selenium IDE and Web Scraping

Selenium is a browser automation framework that includes IDE, Remote Control server and bindings of various flavors including Java, .Net, Ruby, Python and other. In this post we touch on the basic structure of the framework and its application to  Web Scraping.
What is Selenium IDE


Selenium IDE is an integrated development environment for Selenium scripts. It is implemented as a Firefox plugin, and it allows recording browsers’ interactions in order to edit them. This works well for software tests, composing and debugging. The Selenium Remote Control is a server specific for a particular environment; it causes custom scripts to be implemented for controlled browsers. Selenium deploys on Windows, Linux, and iOS. How various Selenium components are supported with major browsers read here.
What does Selenium do and Web Scraping

Basically Selenium automates browsers. This ability is no doubt to be applied to web scraping. Since browsers (and Selenium) support JavaScript, jQuery and other methods working with dynamic content why not use this mix for benefit in web scraping, rather than to try to catch Ajax events with plain code? The second reason for this kind of scrape automation is browser-fasion data access (though today this is emulated with most libraries).

Yes, Selenium works to automate browsers, but how to control Selenium from a custom script to automate a browser for web scraping? There are Selenium PHP and other language libraries (bindings) providing for scripts to call and use Selenium. It is possible to write Selenium clients (using the libraries) in almost any language we prefer, for example Perl, Python, Java, PHP etc. Those libraries (API), along with a server, the Java written server that invokes browsers for actions, constitute the Selenum RC (Remote Control). Remote Control automatically loads the Selenium Core into the browser to control it. For more details in Selenium components refer to here.


A tough scrape task for programmer

“…cURL is good, but it is very basic.  I need to handle everything manually; I am creating HTTP requests by hand.
This gets difficult – I need to do a lot of work to make sure that the requests that I send are exactly the same as the requests that a browser would
send, both for my sake and for the website’s sake. (For my sake
because I want to get the right data, and for the website’s sake
because I don’t want to cause error messages or other problems on their site because I sent a bad request that messed with their web application).  And if there is any important javascript, I need to imitate it with PHP.
It would be a great benefit to me to be able to control a browser like Firefox with my code. It would solve all my problems regarding the emulation of a real browser…
it seems that Selenium will allow me to do this…” -Ryan S

Yes, that’s what we will consider below.
Scrape with Selenium

In order to create scripts that interact with the Selenium Server (Selenium RC, Selenium Remote Webdriver) or create local Selenium WebDriver script, there is the need to make use of language-specific client drivers (also called Formatters, they are included in the selenium-ide-1.10.0.xpi package). The Selenium servers, drivers and bindings are available at Selenium download page.
The basic recipe for scrape with Selenium:

    Use Chrome or Firefox browsers
    Get Firebug or Chrome Dev Tools (Cntl+Shift+I) in action.
    Install requirements (Remote control or WebDriver, libraries and other)
    Selenium IDE : Record a ‘test’ run thru a site, adding some assertions.
    Export as a Python (other language) script.
    Edit it (loops, data extraction, db input/output)
    Run script for the Remote Control

The short intro Slides for the scraping of tough websites with Python & Selenium are here (as Google Docs slides) and here (Slide Share).
Selenium components for Firefox installation guide

For how to install the Selenium IDE to Firefox see  here starting at slide 21. The Selenium Core and Remote Control installation instructions are there too.
Extracting for dynamic content using jQuery/JavaScript with Selenium

One programmer is doing a similar thing …

1. launch a selenium RC (remote control) server
2. load a page
3. inject the jQuery script
4. select the interested contents using jQuery/JavaScript
5. send back to the PHP client using JSON.

He particularly finds it quite easy and convenient to use jQuery for
screen scraping, rather than using PHP/XPath.
Conclusion

The Selenium IDE is the popular tool for browser automation, mostly for its software testing application, yet also in that Web Scraping techniques for tough dynamic websites may be implemented with IDE along with the Selenium Remote Control server. These are the basic steps for it:

    Record the ‘test‘ browser behavior in IDE and export it as the custom programming language script
    Formatted language script runs on the Remote Control server that forces browser to send HTTP requests and then script catches the Ajax powered responses to extract content.

Selenium based Web Scraping is an easy task for small scale projects, but it consumes a lot of memory resources, since for each request it will launch a new browser instance.



Source: http://extract-web-data.com/selenium-ide-and-web-scraping/

Data Mining, Not Just a Method But a Technique

Web data mining is segregating probable clients out of huge information available on the Internet by performing various searches. It could be well organized and structured, or raw, depending on the use of the data. Web data mining could be done using a simple database program or investing money in a costly program.

Start collecting basic contact information of probable clients, such as: names, addresses, landline and cell phone numbers, email addresses and education or occupation if required.

CART and CHAID data mining

While collecting data you will find that tree-shaped structures that represent decisions. These derived decisions give rules for the classification of data collected. Precise decision tree methods include Classification and Regression Trees also know as CART data mining and Chi Square Automatic Interaction Detection also known as CHAID data mining. CART and CHAID data mining are decision tree techniques used for classification of data collected. They provide a set of rules that could be applied to unclassified data collected in prediction. CART segments a dataset creating two-way splits whereas CHAID segments using chi square tests creating multi-way splits. CART requires less data preparation compared to CHAID.

Understanding customer's actions

Keep a track of customer's actions like: what does he buy, when does he buy, why does he buy, what is the use of his buying, etc. Knowing such simple things about your customer will help you to understand needs of your customer better and thus process of data mining services will be easier and quality data would be mined. This will increase your personal relations with your customer which would finally result in a better professional relationship.

Following demography

Mine the data as per demography, dependent on geography as well as socio economic background of business location. You can use government statistics as the source of your data collection. Keeping it in mind you can go ahead with the understanding of the community existing and thus the data required.

Use your informal conversation in serving your clients better

Use minute details of your conversation and understanding with your customers to serve them. If essential, conduct surveys, send a professional gift or use some other object that helps you understand better in fulfilling customer needs. This will increase the bonding between you and your customer and you will be able to serve your customer better in providing data mining services.

Insert the collect information in a desktop database. More the information is collected you will find that you can prepare specific templates in feeding information. Using a desktop database, it is easier to make changes later on as and when required.

Maintaining privacy

While performing, it is essential to ensure that you or your team members are not violating privacy laws in gathering or providing the data information. Once trust is lost, you may also loose the customer, because trust is the base of any relationship, let it be a business relation.




Source: http://ezinearticles.com/?Data-Mining,-Not-Just-a-Method-But-a-Technique&id=5416129

Monday, 23 September 2013

How Web Data Extraction Services Will Save Your Time and Money by Automatic Data Collection

Data scrape is the process of extracting data from web by using software program from proven website only. Extracted data any one can use for any purposes as per the desires in various industries as the web having every important data of the world. We provide best of the web data extracting software. We have the expertise and one of kind knowledge in web data extraction, image scrapping, screen scrapping, email extract services, data mining, web grabbing.

Who can use Data Scraping Services?

Data scraping and extraction services can be used by any organization, company, or any firm who would like to have a data from particular industry, data of targeted customer, particular company, or anything which is available on net like data of email id, website name, search term or anything which is available on web. Most of time a marketing company like to use data scraping and data extraction services to do marketing for a particular product in certain industry and to reach the targeted customer for example if X company like to contact a restaurant of California city, so our software can extract the data of restaurant of California city and a marketing company can use this data to market their restaurant kind of product. MLM and Network marketing company also use data extraction and data scrapping services to to find a new customer by extracting data of certain prospective customer and can contact customer by telephone, sending a postcard, email marketing, and this way they build their huge network and build large group for their own product and company.

We helped many companies to find particular data as per their need for example.

Web Data Extraction

Web pages are built using text-based mark-up languages (HTML and XHTML), and frequently contain a wealth of useful data in text form. However, most web pages are designed for human end-users and not for ease of automated use. Because of this, tool kits that scrape web content were created. A web scraper is an API to extract data from a web site. We help you to create a kind of API which helps you to scrape data as per your need. We provide quality and affordable web Data Extraction application

Data Collection

Normally, data transfer between programs is accomplished using info structures suited for automated processing by computers, not people. Such interchange formats and protocols are typically rigidly structured, well-documented, easily parsed, and keep ambiguity to a minimum. Very often, these transmissions are not human-readable at all. That's why the key element that distinguishes data scraping from regular parsing is that the output being scraped was intended for display to an end-user.

Email Extractor

A tool which helps you to extract the email ids from any reliable sources automatically that is called a email extractor. It basically services the function of collecting business contacts from various web pages, HTML files, text files or any other format without duplicates email ids.

Screen scrapping

Screen scraping referred to the practice of reading text information from a computer display terminal's screen and collecting visual data from a source, instead of parsing data as in web scraping.

Data Mining Services

Data Mining Services is the process of extracting patterns from information. Datamining is becoming an increasingly important tool to transform the data into information. Any format including MS excels, CSV, HTML and many such formats according to your requirements.

Web spider

A Web spider is a computer program that browses the World Wide Web in a methodical, automated manner or in an orderly fashion. Many sites, in particular search engines, use spidering as a means of providing up-to-date data.

Web Grabber

Web grabber is just a other name of the data scraping or data extraction.

Web Bot

Web Bot is software program that is claimed to be able to predict future events by tracking keywords entered on the Internet. Web bot software is the best program to pull out articles, blog, relevant website content and many such website related data We have worked with many clients for data extracting, data scrapping and data mining they are really happy with our services we provide very quality services and make your work data work very easy and automatic.




Source: http://ezinearticles.com/?How-Web-Data-Extraction-Services-Will-Save-Your-Time-and-Money-by-Automatic-Data-Collection&id=5159023

Friday, 20 September 2013

Basics of Online Web Research, Web Mining & Data Extraction Services

The evolution of the World Wide Web and Search engines has brought the abundant and ever growing pile of data and information on our finger tips. It has now become a popular and important resource for doing information research and analysis.

Today, Web research services are becoming more and more complicated. It involves various factors such as business intelligence and web interaction to deliver desired results.

Web Researchers can retrieve web data using search engines (keyword queries) or browsing specific web resources. However, these methods are not effective. Keyword search gives a large chunk of irrelevant data. Since each webpage contains several outbound links it is difficult to extract data by browsing too.

Web mining is classified into web content mining, web usage mining and web structure mining. Content mining focuses on the search and retrieval of information from web. Usage mining extract and analyzes user behavior. Structure mining deals with the structure of hyperlinks.

Web mining services can be divided into three subtasks:

Information Retrieval (IR): The purpose of this subtask is to automatically find all relevant information and filter out irrelevant ones. It uses various Search engines such as Google, Yahoo, MSN, etc and other resources to find the required information.

Generalization: The goal of this subtask is to explore users' interest using data extraction methods such as clustering and association rules. Since web data are dynamic and inaccurate, it is difficult to apply traditional data mining techniques directly on the raw data.

Data Validation (DV): It tries to uncover knowledge from the data provided by former tasks. Researcher can test various models, simulate them and finally validate given web information for consistency.




Source: http://ezinearticles.com/?Basics-of-Online-Web-Research,-Web-Mining-and-Data-Extraction-Services&id=4511101

Thursday, 19 September 2013

Data Mining in the 21st Century: Business Intelligence Solutions Extract and Visualize

When you think of the term data mining, what comes to mind? If an image of a mine shaft and miners digging for diamonds or gold comes to mind, you're on the right track. Data mining involves digging for gems or nuggets of information buried deep within data. While the miners of yesteryear used manual labor, modern data minors use business intelligence solutions to extract and make sense of data.

As businesses have become more complex and more reliant on data, the sheer volume of data has exploded. The term "big data" is used to describe the massive amounts of data enterprises must dig through in order to find those golden nuggets. For example, imagine a large retailer with numerous sales promotions, inventory, point of sale systems, and a gift registry. Each of these systems contains useful data that could be mined to make smarter decisions. However, these systems may not be interlinked, making it more difficult to glean any meaningful insights.

Data warehouses are used to extract information from various legacy systems, transform the data into a common format, and load it into a data warehouse. This process is known as ETL (Extract, Transform, and Load). Once the information is standardized and merged, it becomes possible to work with that data.

Originally, all of this behind-the-scenes consolidation took place at predetermined intervals such as once a day, once a week, or even once a month. Intervals were often needed because the databases needed to be offline during these processes. A business running 24/7 simply couldn't afford the down time required to keep the data warehouse stocked with the freshest data. Depending on how often this process took place, the data could be old and no longer relevant. While this may have been fine in the 1980s or 1990s, it's not sufficient in today's fast-paced, interconnected world.

Real-time EFL has since been developed, allowing for continuous, non-invasive data warehousing. While most business intelligence solutions today are capable of mining, extracting, transforming, and loading data continuously without service disruptions, that's not the end of the story. In fact, data mining is just the beginning.

After mining data, what are you going to do with it? You need some form of enterprise reporting in order to make sense of the massive amounts of data coming in. In the past, enterprise reporting required extensive expertise to set up and maintain. Users were typically given a selection of pre-designed reports detailing various data points or functions. While some reports may have had some customization built in, such as user-defined date ranges, customization was limited. If a user needed a special report, it required getting someone from the IT department skilled in reporting to create or modify a report based on the user's needs. This could take weeks - and it often never happened due to the hassles and politics involved.

Fortunately, modern business intelligence solutions have taken enterprise reporting down to the user level. Intuitive controls and dashboards make creating a custom report a simple matter of drag and drop while data visualization tools make the data easy to comprehend. Best of all, these tools can be used on demand, allowing for true, real-time ad hoc enterprise reporting.

Frank Poladi is the author of this article about data mining in the 21st century. In this article he gives his readers insight on the world of data mining and using it with business intelligence solutions. He notes that to make sense of all this data enterprise reporting is a major factor as well.




Source: http://ezinearticles.com/?Data-Mining-in-the-21st-Century:-Business-Intelligence-Solutions-Extract-and-Visualize&id=7504537

Tuesday, 17 September 2013

Data Mining And Importance to Achieve Competitive Edge in Business

What is data mining? And why it is so much importance in business? These are simple yet complicated questions to be answered, below is brief information to help understanding data and web mining services.

Mining of data in general terms can be elaborated as retrieving useful information or knowledge for further process of analyzing from various perspectives and summarizing in valuable information to be used for increasing revenue, cut cost, to gather competitive information on business or product. And data abstraction finds a great importance in business world as it help business to harness the power of accurate information thus providing competitive edge in business. May business firms and companies have their own warehouse to help them collect, organize and mine information such as transactional data, purchase data etc.

But to have a mining services and warehouse at premises is not affordable and not very cost effective to solution for reliable information solutions. But as if taking out of information is the need for every business now days. Many companies are providing accurate and effective data and web data mining solutions at reasonable price.

Outsourcing information abstraction services are offered at affordable rates and it is available for wide range of data mine solutions:

• taking out business data
• service to gather data sets
• digging information of datasets
• Website data mining
• stock market information
• Statistical information
• Information classification
• Information regression
• Structured data analysis
• Online mining of data to gather product details
• to gather prices
• to gather product specifications
• to gather images

Outsource web mining solutions and data gathering solutions has been effective in terms of cost cutting, increasing productivity at affordable rates. Benefits of data mining services include:

• clear customer, service or product understanding
• less or minimal marketing cost
• exact information on sales, transactions
• detection of beneficial patterns
• minimizing risk and increased ROI
• new market detection
• Understanding clear business problems and goals

Accurate data mining solutions could prove to be an effective way to cut down cost by concentrating on right place.

We are online web research company specialized in providing comprehensive web based online research services and data mining services. We are providing high quality and accurate online web research services with an expertise in the field from last 17 years. For more details can visit our website: http://www.onlinewebresearchservices.com




Source: http://ezinearticles.com/?Data-Mining-And-Importance-to-Achieve-Competitive-Edge-in-Business&id=5771888

Monday, 16 September 2013

Data Mining - A Short Introduction

Data mining is an integral part of data analysis which contains a series of activities that goes from the 'meaning' of the ideas, to the 'analysis' of the data and up to the 'interpretation' and 'evaluation' of the outcome. The different stages of the technique are as follows:

Objectives for Analysis: It is sometimes very difficult to statistically define the phenomenon we wish to analyze. In fact, the business objectives are often clear, but the same can be difficult to formalize. A clear understanding of the crisis and the goals is very important setup the analysis correctly. This is undoubtedly, one of the most complex parts of the process, since it establishes the techniques to be engaged and as such, the objectives must be crystal clear and there should not be any doubt or ambiguity.

Collection, grouping and pre-processing of the data: Once the objectives of the analysis are set and defined, we need to gather or choose the data needed for the study. At first, it is essential to recognize the data sources. Usually data are collected from the internal sources as the same are economical and more dependable and moreover these data also has the benefit of being the outcome of the experiences and procedures of the business itself.

Investigative analysis of the data and their conversion: This stage includes a preliminary examination of the information available. It involves a preliminary assessment of the significance of the gathered data. An exploratory and / or investigative analysis can highlight the irregular data. An exploratory analysis is important because it lets the analyst choose the most suitable statistical method for the subsequent stage of the analysis.

Choosing statistical methods: There are multiple statistical methods that can be put into use for the purpose of analysis, so it is very essential to categorize the existing methods. The choice statistical method is case specific and depends on the problem and also upon the type of information available.

Data analysis on the basis of chosen methods: Once the statistical method is chosen, the same must be translated into proper algorithms for working out the results. Ranges of specialized and non-specialized software are widely available for data mining and as such it is not always required to develop ad hoc computation algorithms for the most 'standard' purpose. However, it is essential that the people managing the data mining method well aware and have a good knowledge and understanding of the various methods of data analysis and also the different software solutions available for the same, so that they may adapt the same in times of need of the company and can flawlessly interpret the results.

Assessment and contrast of the techniques used and selection of the final model for analysis: It is of utmost necessity to choose the best 'model' from the variety of statistical methods accessible. The selection of the model should be based in contrast with the results obtained. When assessing the performance of a specific statistical method and / or type, all other dependent and / or relevant criterions should also be considered. The other criterions may be the constraints on the company both in terms of time and resources or it may be in terms of quality and the accessibility of data.

Elucidation of the selected statistical model and its employment in the decision making process: The scope of data mining is not limited to data analysis rather it is also includes the integration of the results so as to facilitate the decision making process of the company. Business awareness, the pulling out of rules and their use in the decision process allows us to proceed from the diagnostic phase to the phase of decision making. Once the model is finalized and tested with an information set, the categorization rule can be generalized. But the inclusion of the data mining process in the business should not be done in haste; rather the same should always be done slowly, setting out sensible and logical aims. The final aim of data mining is to be an integral supporting part of the company's decision making process.



Source: http://ezinearticles.com/?Data-Mining---A-Short-Introduction&id=6573285

Saturday, 14 September 2013

Offline Data Mining Strikes Gold

You'll often hear the term "striking gold" associated with data mining. Just as gold miners received information about a patch of land and went in with their shovels hoping to strike it rich, data mining deals in relatively the same way. The process is being popular for businesses of various types, and if done right it can be an extremely low-risk, high-reward process.

Basically, data mining is the process of discovering and analyzing data from different perspectives. The process of getting information and facts from usable sources. Once data is compiled and analyzed, it is then summarized into useful information for a business. The result, hopefully, will help to cut overhead costs, increase revenue and be an all-around tool for business improvement. It can be used to improve and generate business strategies that will help you and your business as well.

In a sense, you can think of data mining like election polling. With a strong sample group of voters, proper analysis can paint a picture of who's going to win the election. If you'll notice, however, there's a catch in this process. A person (statistic) would have to be present within a field in order to give a result i.e. a voter would need to be polled instead of a random person.

Anything quantifiable is data. It is a factual information used as a basis for reasoning, discussion, or calculation. It is most basically anything and everything under the sun. You can deal with facts, numbers, text, people, and even statistics on shopping habits. Just about a bit of everything.

Businesses are pressing the limits of what data is, using operational data like cost, inventory, payroll, accounting and sales; non-operational data like forecast data, macro economic data and industry sales; and even meta-data, which is, essentially, data about the collected data.

Any collected information can then be quantified to knowledge, and trends can be discovered and predicted. The goal is to mine the data, analyze it and come up with hard data about consumer buying behaviors, employee behavior, geographical significance, and a number of other usable statistics to help your business grow.

Not every business is employing this process on the same scale. While some do collect the data in various forms and use it to their advantage, only the companies serious about data mining actually invest in the processing power and build data warehouses where trends are stored and all data is centralized.




Source: http://ezinearticles.com/?Offline-Data-Mining-Strikes-Gold&id=6266733

Friday, 13 September 2013

Data Mining and the Tough Personal Information Privacy Sell Considered

Everyone come on in and have a seat, we will be starting this discussion a little behind schedule due to the fact we have a full-house here today. If anyone has a spare seat next to them, will you please raise your hands, we need to get some of these folks in back a seat. The reservations are sold out, but there should be a seat for everyone at today's discussion.

Okay everyone, I thank you and thanks for that great introduction, I just hope I can live up to all those verbal accolades.

Oh boy, not another controversial subject! Yes, well, surely you know me better than that by now, you've come to expect it. Okay so, today's topic is one about the data mining of; Internet Traffic, Online Searches, Smart Phone Data, and basically, storing all the personal data about your whole life. I know, you don't like this idea do you - or maybe you participate online in social online networks and most of your data is already there, and you've been loading up your blog with all sorts of information?

Now then, contemporary theory and real world observation of the virtual world predicts that for a fee, or for a trade in free services, products, discounts, or a chance to play in social online networks, employment opportunity leads, or the prospects of future business you and nearly everyone will give up some personal information.

So, once this data is collected, who will have access to it, who will use it, and how will they use it? All great questions, but first how can the collection of this data be sold to the users, and agreed upon in advance? Well, this can at times be very challenging; yes, very tough sell, well human psychology online suggests that if we give benefits people will trade away any given data of privacy.

Hold That Thought.

Let's digress a second, and have a reality check dialogue, and will come back to that point above soon enough, okay - okay agreed then.

The information online is important, and it is needed at various national security levels, this use of data is legitimate and worthy information can be gained in that regard. For instance, many Russian Spies were caught in the US using social online networks to recruit, make business contacts, and study the situation, makes perfect sense doesn't it? Okay so, that particular episode is either; an excuse to gather this data and analyze it, or it is a warning that we had better. Either way, it's a done deal, next topic.

And, there is the issue with foreign spies using the data to hurt American businesses, or American interests, or even to undermine the government, and we must understand that spies in the United States come from over 70 other nations. And let's not dismiss the home team challenge. What's that you ask? Well, we have a huge intelligence industrial complex and those who work in and around the spy business, often freelance on the side for Wall Street, corporations, or other interests. They have access to information, thus all that data mined data is at their disposal.

Is this a condemnation of sorts; No! I am merely stating facts and realities behind the curtain of created realities of course, without judgment, but this must be taken into consideration when we ask; who can we trust with all this information once it is collected, stored, and in a format which can be sorted? So, we need a way to protect this data for the appropriate sources and needs, without allowing it to be compromised - this must be our first order of business.

Let's Undigress and Go Back to the Original Topic at hand, shall we? Okay, deal.

Now then, what about large corporate collecting information; Proctor and Gamble, Ford, GM, Amazon, etc? They will certainly be buying this data from social networks, and in many cases you've already given up your rights to privacy merely by participating. Of course, all the data will help these companies refine their sorts using your preferences, thus, the products or services they pitch you will be highly targeted to your exact desires, needs, and demographics, which is a lot better than the current bombardment of Viagra Ads with disgusting titles, now in your inbox, deleted junk files.

Look, here is the deal...if we are going to collect data online, through social networks, and store all that the data, then we also need an excuse to collect the data first place, or the other option is not tell the public and collect it anyway, which we already probably realize that is now being done in some form or fashion. But let's for the sake of arguments say it isn't, then should we tell the public we are doing, or are going to do this. Yes, however if we do not tell the public they will eventually figure it out, and conspiracy theories will run rampant.

We already know this will occur because it has occurred in the past. Some say that when any data is collected from any individual, group, company, or agency, that all those involved should also be warned on all the collection of data, as it is being collected and by whom. Including the NSA, a government, or a Corporation which intends on using this data to either sell you more products, or for later use by their artificial intelligence data scanning tools.

Likewise, the user should be notified when cookies are being used in Internet searchers, and what benefits they will get, for instance; search features to help bring about more relevant information to you, which might be to your liking. Such as Amazon.com which tracks customer inquiries and brings back additional relevant results, most online shopping eCommerce sites do this, and there was a very nice expose on this in the Wall Street Journal recently.

Another digression if you will, and this one is to ask a pertinent question; If the government or a company collects the information, the user ought to know why, and who will be given access to this information in the future, so let's talk about that shall we? I thought you might like this side topic, good for you, it shows you also care about these things.

And as to that question, one theory is to use a system that allows certain trusted sources in government, or corporations which you do business with to see some data, then they won't be able to look without being seen, and therefore you will know which government agencies, and which corporations are looking at your data, and therefore there will be transparency, and there would have to be at that point justification for doing so. Or most likely folks would have a fit and then, a proverbial field day with the intrusion in the media.

Now then, one recent report from the government asks the dubious question; "How do we define the purpose for which the data will be used?"

Ah ha, another great question in this on-going saga indeed. It almost sounds as if they too were one of my concerned audience members, or even a colleague. Okay so, it is important not only to define the purpose of the data collection, but also to justify it, and it better be good. Hey, I see you are all smiling now. Good, because, it's going to get a bit more serious on some of my next points here.

Okay, and yes this brings about many challenges, and it is also important to note that there will be, ALWAYS more outlets for the data, which is collected, as time goes on. Therefore the consumer, investor, or citizen who allows their data to be compromised, stored for later use for important issues such as national security, or for corporations to help the consumer (in this case you) in their purchasing decisions, or for that company's planning for inventory, labor, or future marketing (most likely; again to whom; ha ha ha, yes you are catching on; You.

Thus, shouldn't you be involved at every step of the way; Ah, a resounding YES! I see from our audience today, and yes, I would have expected nothing less from you either. And as all this process takes place, eventually "YOU" are going to figure out that this data is out of control, and ends up everywhere. So, should you give away data easily?

No, and if it is that valuable, hold out for more. And then, you will be rewarded for the data, which is yours, that will be used on your behalf and potentially against you in some way in the future; even if it is only for additional marketing impressions on the websites you visit or as you walk down the hallway at the mall;

"Let's see a show of hands; who has seen Minority Report? Ah, most of you, indeed, if you haven't go see, it and you will understand what we are all saying up here, and others are saying in the various panel discussions this weekend."

Now you probably know this, but the very people who are working hard to protect your data are in fact the biggest purveyors of your information, that's right our government. And don't get me wrong, I am not anti-government, just want to keep it responsible, as much is humanly possible. Consider if you will all the data you give to the government and how much of that public record is available to everyone else;

    Tax forms to the IRS,
    Marriage licenses,
    Voting Registration,
    Selective Services Card,
    Property Taxes,
    Business Licenses,
    Etc.

The list is pretty long, and the more you do, the more information they have, and that means the more information is available; everywhere, about who; "YOU! That's who!" Good I am glad we are all clear on that one. Yes, indeed, all sorts of things, all this information is available at the county records office, through the IRS, or with various branches of OUR government. This is one reason we should all take notice to the future of privacy issues. Often out government, but it could be any first world government, claims it is protecting your privacy, but it has been the biggest purveyors of giving away our personal and private data throughout American history. Thus, there will a little bit of a problem with consumers, taxpayers, or citizens if they no longer trust the government for giving away such things as;

    Date of birth,
    Social Security number,
    Driver's license,
    Driving record,
    Taxable information,
    Etc., on and on.

And let's not kid ourselves here all this data is available on anyone, it's all on the web, much of it can be gotten free, some costs a little, never very much, and believe me there is a treasure trove of data on each one of us online. And that's before we look into all the other information being collected now.

Now then, here is one solution for the digital data realm, including smart phone communication data, perhaps we can control and monitor the packet flow of information, whereby all packets of info is tagged, and those looking at the data will also be tagged, with no exceptions. Therefore if someone in a government bureaucracy is looking at something they shouldn't be looking at, they will also be tagged as a person looking for the data.

Remember the big to do about someone going through Joe The Plumber's records in OH, or someone trying to release sealed documents on President Bush's DUI when he was in his 20s, or the fit of rage by Sara Palin when someone hacked her Yahoo Mail Account, or when someone at a Hawaii Hospital was rummaging through Barak Obama's certificate of showing up at the hospital as a baby, with mother in tow?

We need to know who is looking at the data, and their reason better be good, the person giving the data has a right-to-know. Just like the "right-to-know" laws at companies, if there are hazardous chemicals on the property. Let me speak on another point; Border Security. You see, we need to know both what is coming and going if we are to have secure borders.

You see, one thing they found with our border security is it is very important not only what comes over the border, which we do need to monitor, but it's also important to see what goes back over the border the other way. This is how authorities have been able to catch drug runners, because they're able to catch the underground economy and cash moving back to Mexico, and in holding those individuals, to find out whom they work for - just like border traffic - our information goes both ways, if we can monitor for both those ways, it keeps you happier, and our data safer.

Another question is; "How do we know the purpose for data being collected, and how can the consumer or citizen be sure that mass data releases will not occur, it's occurred in almost every agency, and usually the citizens are warned that their data was released or that the data base containing their information was breached, but that's after the fact, and it just proves that data is like water, and it's hard to contain. Information wants to be free, and it will always find a way to leak out, especially when it's in the midst of humans.

Okay, I see my time is running short here, let me go ahead and wrap it up and drive through a couple main points for you, then I'll open it up for questions, of which I don't doubt there will be many, that's good, and that means you've been paying attention here today.

It appears that we need to collect data for national security purposes research, planning, and for IT system for future upgrades. And collecting data for upgrades of an IT system, you really need to know about the bulk transfers of data and the time, which that data flows, and therefore it can be anonymized.

For national security issues, and for their research, that data will have anomalies in it, and there are problems with anomalies, because can project a false positives, and to get it right they have to continually refine it all. And although this may not sit well with most folks, nevertheless, we can find criminals this way, spies, terrorist cells, or those who work to undermine our system and stability of our nation.

With regards to government and the collection of data, we must understand that if there are bad humans in the world, and there are. And if many of those who shall seek power, may not be good people, and since information is power, you can see the problem, as that information and power will be used to help them promote their own agenda and rise in power, but it undermines the trust of the system of all the individuals in our society and civilization.

On the corporate front, they are going to try to collect as much data on you as they can, they've already started. After all, that's what the grocery stores are doing with their rewards program if you hadn't noticed. Not all the information they are collecting they will ever use, but they may sell it to third part affiliates, partners, or vendors, so that's at issue. Regulation will be needed in this regard, but the consumer should also have choices, but they ought to be wise about those choices and if they choose to give away personal information, they should know the risks, rewards, consequences, and challenges ahead.

Indeed, I thank you very much, and be sure to pick up a handout on your way out, if you didn't already get one, from the good looking blonde, Sherry, at the door. Thanks again, and let's take a 5-minute break, and then head into the question and answer session, deal?




Source: http://ezinearticles.com/?Data-Mining-and-the-Tough-Personal-Information-Privacy-Sell-Considered&id=4868392

Thursday, 12 September 2013

Spatial Data Mining Systems

Data mining systems are used for a variety of different purposes. Essentially, large amounts of data are stored in one particular spot, enabling organizations and companies to access information that will help them in their own marketing and surveillance strategies. By having access to all relevant data, a company can better employ their sales and production tactics. Companies and businesses can save large sums of money by researching past consumer behaviors and producing product in relation to how well it sold at certain times. This is just a small example of what data mining can do for a company.

Spatial data mining systems rely on the same principals. However, the data stored is related directly to special data. Spatial data mining systems are also used to detect patterns, but the patterns that are being looked for are geographical patterns. Up until this point geographical information systems and spatial data mining have existed as two separate technologies. Both systems have their own individual approaches to storing geographical data. Each system has derived from its own methods and traditions, making it difficult to cross the two. Geographical information systems tend to be much more basic and only provide the most simple form of functionality. Because there became a larger demand for geographically referenced data, the basic functions of GIS represented the massive need for more sophisticated methods of mining spatial data. There is a larger demand for geographical analysis and modeling as well as digital mapping and remote sensing.

Through spatial data mining, there have been numerous benefits experienced by those who make important decisions based on geographical information systems. Public and private sector organizations have recently become aware of the huge potential of the amount of information they possess in their thematic and geographical referenced databases. There are various types of companies who can benefit from geographical data. For example, those that are in the public health sector will use this data to determine the cause for epidemics such as disease clusters. In addition, some environmental agencies will use the information collected in these databases to understand the impact of land-use patterns that are in constant flux and how they relate to climate change. Geo-marketing companies will also find this information useful when they are conducting customer research regarding segmentation on spatial location.

However, spatial data mining systems force those who need them to face certain challenges. First of all, these databases tend to be extremely large and can be cumbersome to sort through when looking for specific information. Geographical information system datasets that already exist are usually split into featured and attributed components and this means that they are separated into hybrid data management systems. Both featured and attributed data systems require separate means of management. For example algorithmic requirements differ when it comes to relational data, which is in the attribute category and for topographical data, which falls under the feature category.

The two main systems for spatial data management are the raster and the vector. Depending on the needs of the data being used, it is important to analyze the benefits and downfalls of both systems.

Doing business in the 21st century doesn't have to be difficult - companies can enhance their marketing procedures through address validation software and various other list cleaning procedures so that they can target their market perfectly!




Source: http://ezinearticles.com/?Spatial-Data-Mining-Systems&id=4792735

Tuesday, 10 September 2013

Data Mining: Its Description and Uses

Data mining also known as the process of analyzing the KDD which stands for Knowledge Discovery in Databases is a part of statistics and computer science. It is a process which aims to find out many various patterns in enormous sets of relational data.

It uses ways at the fields of machine learning, database systems, artificial intelligence, and statistics. It permits users to examine data from many various perspectives, sort it, and summarize the identified relationships.

In general, the objective of data mining process is to obtain info out of a data set and convert it into a comprehensible outline. Also, it includes the following: data processing, data management and database aspects, visualization, complexity considerations, online updating, inference and model considerations, and interestingness metrics.

On the other hand, the actual data mining assignment is the semi-automatic or automatic exploration of huge quantities of information to extract patterns that are interesting and previously unknown. Such patterns can be the unusual records or the anomaly detection, data records groups or the cluster analysis, and the dependencies or the association rule mining. Usually, this involves utilizing database methods like spatial indexes. Such patters could be perceived as a type of summary of input data, and could be used in further examination or, for example, in predictive analysis and machine learning.

Today, data mining is utilized by different consumer-focused companies like those in the financial, retails, marketing, and communications fields. It permits such companies to find out relationships among the internal aspects like staff skills, price, product positioning, and external aspects like customer information, competition, and economic indicators. Additionally, it allows them to define the effect on corporate profits, sales, and customer satisfaction; and dig into the summary information to be able to see transactional data in detail.

With data mining process, a retailer can make use of point-of-scale customer purchases records to send promotions based on the purchase history of a client. The retailer can improve products and campaigns or promotions that can be appealing to a definite customer group by using mining data from comment cards.

Generally, any of the following relationships are obtained.

1. Associations: Data could be mined to recognize associations.
2. Clusters: Data are sorted based on a rational relationships or consumer preferences.
3. Sequential Patters: Data is mined to expect patterns and trends in behavior.
4. Classes: Data that are stored are utilized to trace data in predetermined segments.



Source: http://ezinearticles.com/?Data-Mining:-Its-Description-and-Uses&id=7252273

Monday, 9 September 2013

Benefits and Advantages of Data Mining

One definition given to data mining is the categorization of information according to the needs and preferences of the user. In data mining, you try to find patterns within a big volume of available data. It is a potent and popular technology for different industries. Data mining can even be compared to the difficult task of looking for a needle in the haystack. The greatest challenge is not obtaining information but uncovering connections and information that have not been known in the past.

Yet, data mining tools can only be utilized efficiently provided you possess huge amounts of information in repository. Almost all of corporate organizations already hold this information. One good example is the list of potential clients for marketing purposes. These are the consumers to whom you can sell commodities or services. You have greater chances of generating more revenues if you know these potential customers in the inventory and determine consumption behavior. There are benefits that you need to know regarding data mining.

    Data mining is not only for entrepreneurs. The process is cut out for analysis as well and can be employed by government agencies, non-profit organizations, and basketball teams. In short, the data must be made more specific and refined according to the needs of the group concerned.

    This unique method can be used along with demographics. Data mining combined with demographics enables enterprises to pursue the advertising strategy for specific segments of customers. That form of advertising that is related directly to behavior.

    It has a flexible nature and can be used by business organizations that focus on the needs of customers. Data mining is one of the more relevant services because of the fast-paced and instant access to information together with techniques in economic processing.

However, you need to prepare ahead of time the data used for mining. It is essential to understand the principles of clustering and segmentation. These two elements play a vital part in marketing campaigns and customer interface. These components encompass the purchasing conduct of consumers over a particular duration. You will be able to separate your customers into categories based on the earnings brought to your company. It is possible to determine the income that these customers will generate and retention opportunities. Simply remember that nearly all profit-oriented entities will desire to maintain high-value and low-risk clients. The target is to ensure that these customers keep on buying for the long-term.



Source: http://ezinearticles.com/?Benefits-and-Advantages-of-Data-Mining&id=7747698

Saturday, 7 September 2013

Data Mining Social Networks, Smart Phone Data, and Other Data Base, Yet Maintaining Privacy

Is it possible to data mine social networks in such a way to does not hurt the privacy of the individual user, and if so, can we justify doing such? It wasn't too long ago the CEO of Google stated that it was important that they were able to keep data of Google searches so they can find disease, flu, and food born medical clusters. By using this data and studying the regions in the searches to help fight against outbreaks of diseases, or food borne illnesses in the distribution system. This is one good reason to store the data, and collect it for research, as long as it is anonomized, then theoretically no one is hurt.

Unfortunately, this also scares the users, because they know if the searches are indeed stored, this data can be used against them in the future, for instance, higher insurance rates, bombardment of advertising, or get them put onto some sort of future government "thought police" watch-list. Especially considering all the political correctness, and new ways of defining hate speech, bullying, and what is, what isn't, and what might be a domestically home-grown terrorist. The future concept of the thought police is very scary to most folks.

Usually if you want to collect data from a user, you have to give them something back in return, and therefore they are willing to sign away certain privacy rights on that data in trade for the use of such services; such as on their cell phone, perhaps a free iPhone app or a virtual product in an online social network.

Artificially Intelligent Search Features

It is no surprised that AI search features are getting smarter, even able to anticipate your next search question, or what you are really trying to ask, even second guessing your question for instance. Now then, let's discuss this for a moment. Many folks very much enjoy the features of Amazon.com search features, which use artificial intelligence to recommend potential other books, which they might be interested in. And therefore the user probably does not mind giving away information about itself, for this upgraded service or ability, nor would the person mind having cookies put onto their Web browser.

Nevertheless, these types of systems are always exploited for other purposes. For instance consider the Federal Trade Commission's do not call list, and consider how many corporations, political party organizations, and all of their affiliates and partners were able to bypass these rules due to the fact that the consumer or customer had bought something from them in the last six months. This is not what consumers or customers had in mind when they decided they wanted to have this "do not call list" and the resultant and response from the market place, well, it proves we cannot trust the telecommunication companies, their lobbyists, or the insiders within their group (many of which over the years have indeed been somehow connected to the intelligence agencies - AT&T - NSA Echelon for example.)

Now then, this article is in no way to be considered a conspiracy theory, it is just a known fact, yes national security does need access to such information, and often it might be relevant, catching bad guys, terrorists, spies, etc. The NSA is to protect the American People. However, when it comes to the telecommunication companies, their job is to protect shareholder's equity, maximize quarterly profits, expand their business models, and create new profit centers in their corporations.

Thus, such user data will be and has been exploited for future profits against the wishes of the consumer, without the consumer benefiting from free services for lower prices in any way. If there is an explained reason, trade-off, and a monetary consideration, the consumer might feel obliged to have additional calls bothering them while they are at home, additional advertising, and tracking of their preferences for ease of use and suggestions. What types of suggestions?

Well, there is a Starbucks two-blocks from here, turn right, then turn left and it is 200 yards, with parking available; "Sale on Frappachinos for gold-card holders today!" In this case the telecommunication company tracks your location, knows your preferences, and collects a small fee from Starbucks, and you get a free-phone, and 20% off your monthly 4G wireless fee. Is that something a consumer might want; when asked 75% of consumers or smart phone users say; yes. See that point?

In the future smart phones may have data transferred between them, rather than going through a given or closest cell tower. In other words, packets of information may go from your cell phone, to the next nearest cell phone, to another near cell phone, to the person which is intended to receive it. And the data passing through each mobile device, will not be able to read any of the information which was it is not assigned to receive as it wasn't sent to it. By using such a scheme telecommunication companies can expand their services without building more new cell towers, and therefore they can lower the price.

However, it also means that when you lay your cell phone on the table, and it is turned on it would be constantly passing data through it, data which is not yours, and you are not getting paid for that, even though you had to purchase the smart phone. But if the phone was given to you, with a large battery, so it wouldn't go dead during all those transmissions, you probably wouldn't care, as long as your data packets of information were indeed safe and no one else could read them.

This technology exists now, and is being discussed, and consider if you will that the whole strategy of networking smart cell phones or personal tech devices together is nothing new. For instance, the same strategies have been designed for satellites, and to use an analogy, this scheme is very similar to the strategies FedEx uses when it sends packages to the next nearest FedEx office if that is their destination, without sending all of the packages all the way across the country to the central Memphis sort, and then all the way back again. They are saving time, fuel, space, and energy, and if cell phones did this it would save the telecommunication companies mega bucks in the savings of building new cell towers.

As long as you got a free cell phone, which many of us do, unless we have the mega top of the line edition, and if they gave you a long-lasting free battery it is win-win for the user. You probably wouldn't care, and the telecommunication companies could most likely lower the cost of services, and not need to upgrade their system, because they can carry a lot more data, without hundreds of billions of dollars in future investments.

Also a net centric system like this is safer to disruption in the event of an emergency, when emergency communications systems take precedence, putting every cell phone user as secondary traffic at the cell towers, which means their calls may not even get through.

Next, the last thing the telecommunication company would want to do is to data mine that data, or those packets of information from people like a soccer mom calling her son waiting at the bus stop at school. And anyone with a cell phone certainly wouldn't want their packets of information being stolen from them and rerouted because someone near them hacked into the system and had a cell phone that was displaying all of their information.

You can see the problems with all this, but you can also see the incredible economies of scale by making each and every cell phone a transmitter and receiver, which it already is in principle anyway, at least now for all data you send and receive. In the new system, if all the data which is closest by is able to transfer through it, and send that data on its way. The receiving cell phone would wait for all the packets of data were in, and then display the information.

You can see why such a system also might cause people to have a problem with it because of what they call net neutrality. If someone was downloading a movie onto their iPad using a 3G or 4G wireless network, it could tie up all the cell phones nearby that were moving the data through them. In this case, it might upset consumers, but if that traffic could be somewhat delayed by priority based on an AI algorithm decision matrix, something simple, then such a tactic for packet distribution plan might allow for this to occur without disruption from the actual cell tower, meaning everyone would be better off. Therefore we all get information flow faster, more dispersed, and therefore safer from intruders. Please consider all this.



Source: http://ezinearticles.com/?Data-Mining-Social-Networks,-Smart-Phone-Data,-and-Other-Data-Base,-Yet-Maintaining-Privacy&id=4867112

Friday, 6 September 2013

Data Mining

Data mining is the retrieving of hidden information from data using algorithms. Data mining helps to extract useful information from great masses of data, which can be used for making practical interpretations for business decision-making. It is basically a technical and mathematical process that involves the use of software and specially designed programs. Data mining is thus also known as Knowledge Discovery in Databases (KDD) since it involves searching for implicit information in large databases. The main kinds of data mining software are: clustering and segmentation software, statistical analysis software, text analysis, mining and information retrieval software and visualization software.

Data mining is gaining a lot of importance because of its vast applicability. It is being used increasingly in business applications for understanding and then predicting valuable information, like customer buying behavior and buying trends, profiles of customers, industry analysis, etc. It is basically an extension of some statistical methods like regression. However, the use of some advanced technologies makes it a decision making tool as well. Some advanced data mining tools can perform database integration, automated model scoring, exporting models to other applications, business templates, incorporating financial information, computing target columns, and more.

Some of the main applications of data mining are in direct marketing, e-commerce, customer relationship management, healthcare, the oil and gas industry, scientific tests, genetics, telecommunications, financial services and utilities. The different kinds of data are: text mining, web mining, social networks data mining, relational databases, pictorial data mining, audio data mining and video data mining.

Some of the most popular data mining tools are: decision trees, information gain, probability, probability density functions, Gaussians, maximum likelihood estimation, Gaussian Baves classification, cross-validation, neural networks, instance-based learning /case-based/ memory-based/non-parametric, regression algorithms, Bayesian networks, Gaussian mixture models, K-Means and hierarchical clustering, Markov models, support vector machines, game tree search and alpha-beta search algorithms, game theory, artificial intelligence, A-star heuristic search, HillClimbing, simulated annealing and genetic algorithms.

Some popular data mining software includes: Connexor Machines, Copernic Summarizer, Corpora, DocMINER, DolphinSearch, dtSearch, DS Dataset, Enkata, Entrieva, Files Search Assistant, FreeText Software Technologies, Intellexer, Insightful InFact, Inxight, ISYS:desktop, Klarity (part of Intology tools), Leximancer, Lextek Onix Toolkit, Lextek Profiling Engine, Megaputer Text Analyst, Monarch, Recommind MindServer, SAS Text Miner, SPSS LexiQuest, SPSS Text Mining for Clementine, Temis-Group, TeSSI®, Textalyser, TextPipe Pro, TextQuest, Readware, Quenza, VantagePoint, VisualText(TM), by TextAI, Wordstat. There is also free software and shareware such as INTEXT, S-EM (Spy-EM), and Vivisimo/Clusty.



Source: http://ezinearticles.com/?Data-Mining&id=196652

Thursday, 5 September 2013

Data Mining As a Process

The data mining process is also known as knowledge discovery. It can be defined as the process of analyzing data from different perspectives and then summarizing the data into useful information in order to improve the revenue and cut the costs. The process enables categorization of data and the summary of the relationships is identified. When viewed in technical terms, the process can be defined as finding correlations or patterns in large relational databases. In this article, we look at how data mining works its innovations, the needed technological infrastructures and the tools such as phone validation.

Data mining is a relatively new term used in the data collection field. The process is very old but has evolved over the time. Companies have been able to use computers to shift over the large amounts of data for many years. The process has been used widely by the marketing firms in conducting market research. Through analysis, it is possible to define the regularity of customers shopping. How the items are bought. It is also possible to collect information needed for the establishment of revenue increase platform. Nowadays, what aides the process is the affordable and easy disk storage, computer processing power and applications developed.

Data extraction is commonly used by the companies that are after maintaining a stronger customer focus no matter where they are engaged. Most companies are engaged in retail, marketing, finance or communication. Through this process, it is possible to determine the different relationships between the varying factors. The varying factors include staffing, product positioning, pricing, social demographics, and market competition.

A data-mining program can be used. It is important note that the data mining applications vary in types. Some of the types include machine learning, statistical, and neural networks. The program is interested in any of the following four types of relationships: clusters (in this case the data is grouped in relation to the consumer preferences or logical relationships), classes (in this the data is stored and finds its use in the location of data in the per-determined groups), sequential patterns (in this case the data is used to estimate the behavioral patterns and patterns), and associations (data is used to identify associations).

In knowledge discovery, there are different levels of data analysis and they include genetic algorithms, artificial neural networks, nearest neighbor method, data visualization, decision trees, and rule induction. The level of analysis used depends on the data that is visualized and the output needed.

Nowadays, data extraction programs are readily available in different sizes from PC platforms, mainframe, and client/server. In the enterprise-wide uses, size ranges from the 10 GB to more than 11 TB. It is important to note that two crucial technological drivers are needed and are query complexity and, database size. When more data is needed to be processed and maintained, then a more powerful system is needed that can handle complex and greater queries.

With the emergence of professional data mining companies, the costs associated with process such as web data extraction, web scraping, web crawling and web data mining have greatly being made affordable.




Source: http://ezinearticles.com/?Data-Mining-As-a-Process&id=7181033

Tuesday, 3 September 2013

Basics of Online Web Research, Web Mining & Data Extraction Services

The evolution of the World Wide Web and Search engines has brought the abundant and ever growing pile of data and information on our finger tips. It has now become a popular and important resource for doing information research and analysis.

Today, Web research services are becoming more and more complicated. It involves various factors such as business intelligence and web interaction to deliver desired results.

Web Researchers can retrieve web data using search engines (keyword queries) or browsing specific web resources. However, these methods are not effective. Keyword search gives a large chunk of irrelevant data. Since each webpage contains several outbound links it is difficult to extract data by browsing too.

Web mining is classified into web content mining, web usage mining and web structure mining. Content mining focuses on the search and retrieval of information from web. Usage mining extract and analyzes user behavior. Structure mining deals with the structure of hyperlinks.

Web mining services can be divided into three subtasks:

Information Retrieval (IR): The purpose of this subtask is to automatically find all relevant information and filter out irrelevant ones. It uses various Search engines such as Google, Yahoo, MSN, etc and other resources to find the required information.

Generalization: The goal of this subtask is to explore users' interest using data extraction methods such as clustering and association rules. Since web data are dynamic and inaccurate, it is difficult to apply traditional data mining techniques directly on the raw data.

Data Validation (DV): It tries to uncover knowledge from the data provided by former tasks. Researcher can test various models, simulate them and finally validate given web information for consistency.



Source: http://ezinearticles.com/?Basics-of-Online-Web-Research,-Web-Mining-and-Data-Extraction-Services&id=4511101

Sunday, 1 September 2013

Limitations and Challenges in Effective Web Data Mining

Web data mining and data collection is critical process for many business and market research firms today. Conventional Web data mining techniques involve search engines like Google, Yahoo, AOL, etc and keyword, directory and topic-based searches. Since the Web's existing structure cannot provide high-quality, definite and intelligent information, systematic web data mining may help you get desired business intelligence and relevant data.

Factors that affect the effectiveness of keyword-based searches include:
• Use of general or broad keywords on search engines result in millions of web pages, many of which are totally irrelevant.
• Similar or multi-variant keyword semantics my return ambiguous results. For an instant word panther could be an animal, sports accessory or movie name.
• It is quite possible that you may miss many highly relevant web pages that do not directly include the searched keyword.

The most important factor that prohibits deep web access is the effectiveness of search engine crawlers. Modern search engine crawlers or bot can not access the entire web due to bandwidth limitations. There are thousands of internet databases that can offer high-quality, editor scanned and well-maintained information, but are not accessed by the crawlers.

Almost all search engines have limited options for keyword query combination. For example Google and Yahoo provide option like phrase match or exact match to limit search results. It demands for more efforts and time to get most relevant information. Since human behavior and choices change over time, a web page needs to be updated more frequently to reflect these trends. Also, there is limited space for multi-dimensional web data mining since existing information search rely heavily on keyword-based indices, not the real data.

Above mentioned limitations and challenges have resulted in a quest for efficiently and effectively discover and use Web resources. Send us any of your queries regarding Web Data mining processes to explore the topic in more detail.




Source: http://ezinearticles.com/?Limitations-and-Challenges-in-Effective-Web-Data-Mining&id=5012994