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Are you aware of every sub-discipline in the category of computer and information sciences? Perhaps not.

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You might discover something very exciting that you didn't know existed!

 

Data mining is a relatively new term, so it follows there is still some disagreement about a precise definition. At Free-Ed.Net, however, we are more interested in getting things done than quibbling over semantics; so for the time being, I am defining data mining as a process intended to reveal otherwise undisclosed information from an analysis of large or multiple data sources.

Here is a nice description of data mining (as opposed to a formal definition):

As mentioned, our definition of data mining is: the exploration and analysis of data in order to discover patterns, correlations and other regularities - as characterised by the search for Predictability. This can be approached in two principle ways: First, one may posit a particular model to describe a pattern, then test it and validate it on the data. This is the "top down" approach. Alternatively, one may discover a pattern without any a priori model in mind, by letting the data speak for itself".   ~From Stephens and Sukumar, An Introduction to Data Mining

Note:  Newbie, self-directed data miners must be very, very careful with this notion of "letting the data speak for itself." One's credibility can be tarnished for very long time by naively confusing causation with correlation. The following is a "must read" for beginners:



Note: Some of these tutorials might require a PDF reader. If you do not have an the Adobe Acrobat Reader installed on your device, you can download a free copy here. Many also require the Google Play app for mobile devices.


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David L. Heiserman, Editor

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Revised: June 06, 2015