What is Data Mining? Good question.

After a great one week vacation away from the blog, it is good to be back. During my vacation I was asked to explain the difference between data mining and information retrieval; so this post goes.

Here is a standard definition I wrote for a graduate course syllabus to be taught next fall at a local university:

“Data mining or knowledge-discovery in databases (KDD) is the science of extracting useful information from large data sets in order to discover meaningful relationships. KDD is achieved through search, classification, association, clustering, and statistical analysis.”

“Enterprise Mining and Web Mining are examples of KDD. Both can be conducted with machine learning workbenches and information management systems that can search, retrieve, classify, and analyze data. Examples of these are the WEKA workbench and the Google search engine.”

This definition, taken from the graduate course Hands-on Data Mining for Business Intelligence I will be teaching; i.e. if everything goes well and we have enough students.

[Who should be enrolled: computer scientists and business & engineering students.]

I like to give this definition to students so they will know that data mining is not just a computer lab thing, but has practical uses in the workplace.

I like to teach students that data mining is an umbrella term. In a sense, IR can be viewed as a subdiscipline of KDD and this in turn as a subdiscipline of computer sciences.

Others might disagree with this way of perceiving KDD and IR. No matter how you feel about this interpretation KDD cannot be overlooked by anyone. Indeed any field, enterprise, or discipline wherein information relationships are necessary to make a decision or wherein decisions are made based on recognition of patterns must resource to data mining. This covers pretty much all disciplines and workplace environments.

If KDD is good for them, for sure it must be good for SEOs and SEMs.

The perception that a drop out junior or a mom-and-pop can think that one day he/she can decide to get into marketing research and succeed are long ago over. That is, if he/she really want to shine. There is no replacement for a solid education and hard work. One cannot fool with crap knowledge, fallacies, or hearsays 100% of the people 100% of the time as many marketers think.