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By Anand Rajaraman
By Jeffrey David Ullman
Publisher: Cambridge University Press
Print Publication Year: 2011
Online Publication Date:June 2012
Chapter DOI: http://dx.doi.org/10.1017/CBO9781139058452.002
In this intoductory chapter we begin with the essence of data mining and a discussion of how data mining is treated by the various disciplines that contribute to this field. We cover “Bonferroni's Principle,” which is really a warning about overusing the ability to mine data. This chapter is also the place where we summarize a few useful ideas that are not data mining but are useful in understanding some important data-mining concepts. These include the TF.IDF measure of word importance, behavior of hash functions and indexes, and identities involving e, the base of natural logarithms. Finally, we give an outline of the topics covered in the balance of the book.
What is Data Mining?
The most commonly accepted definition of “data mining” is the discovery of “models” for data. A “model,” however, can be one of several things. We mention below the most important directions in modeling.
Statisticians were the first to use the term “data mining.” Originally, “data mining” or “data dredging” was a derogatory term referring to attempts to extract information that was not supported by the data. Section 1.2 illustrates the sort of errors one can make by trying to extract what really isn't in the data. Today, “data mining” has taken on a positive meaning. Now, statisticians view data mining as the construction of a statistical model, that is, an underlying distribution from which the visible data is drawn.