How To Do Data Mining

One way that some corporations keep ahead of their competition is to do data mining.  Businesses derive useful information from huge databases through statistical analysis.

Applications of this mathematical algorithm based analysis tool are in the areas of product analysis, consumer research marketing, e-Commerce, stock investment trend and many more.  Relational database mining, web mining, text mining, audio and video mining, and social networks mining are some types of data mining.
You can relate data mining to geology in the sense that in geology you search for specific minerals (for example gold or lead), while a statistical data miner uses various tools to find useful information from a wide database.  It is a way of extracting data from very large and sometimes complex databases to find patterns or trends that a company can use to further their business.

Data mining is a labor intensive job wherein a lot of data has to be collected and analyzed.  Outsourcing data mining jobs may be more beneficial to companies who do not have the time or manpower to invest in this endeavor.  The outsourcing company will take care of collecting the needed data and organizing the data in a well mapped database so that they can easily filter or extract the required information for analysis.  But if you have the resources, you can also use a variety of data mining programs out there.  Some data mining software are SAS Enterprise Miner, DataDetective, Statistical Data Miner, Statistica, and Weka.

You can read more about data mining on the Internet.  But just to give you an idea, below are the steps in performing data mining:

  1. Define the objectives.   This step is basically identifying why you need to perform data mining.  What problem brought about a perceived data mining solution and what are the objectives for this project?
  2. Gather and organize the data.  The bulk of the work in data mining is data gathering and exploring.  Data has to be organized in an efficient and effective way for you to be able to process the information properly.
  3. Select the data-mining task.  There are four basic data mining techniques: classification, regression, clustering and association rule.  Choose the ones appropriate to your objectives.
  4. Modeling.  This is when you actually perform the data mining procedure.  Search for patterns in the database by applying your selected data mining techniques in order to create models.
  5. Data interpretation and validation.  After the actual data mining task, the data gathered is now interpreted, validated, transformed and visualized using statistical techniques. 
  6. Data deployment.   This step can involve a report that is generated showing the patterns found in the data mining activity or the use of the data model on a larger group of data for further analysis.

Data mining is an iterative process so you may have to go through several of the steps above a number of times until the results you derive answer your objectives.

There was a time when data mining was not widely used by businesses.  Now, public and private companies and organizations find data mining an invaluable way for them to keep up and even get ahead of their competitors.  Businesses are now able to monitor the kind of customers their products cater to and what their customers’ buying behaviors are.   The information mined and modeled from various types of databases is used for competition analysis, market research, economic trending, consumer behavior, industry research, geographical information analysis and so on.  Even the FBI and other law enforcement groups use data mining techniques.


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