Chapter 1. Introduction to Data Mining in SQL Server 2008

It's always necessary to explain exactly what is meant by the term data mining. You would hope that any particular technology has a name that is either absolutely clear as to what it means (such as reporting) or completely devoid of meaning, but catchy, so the association is unique (such as Silverlight). However, this is not the case for data mining. The term data mining has been used to mean anything from ad hoc queries, rules-based notifications, or pivot-chart analysis to evil government domestic-spying programs. As it is used in this book, data mining is the process of analyzing data to find hidden patterns using automatic methodologies. This type of data mining is often referred to using other terms such as machine learning, knowledge discovery in databases (KDD), or predictive analytics. Although each of these terms has a slightly different connotation, they overlap enough to be functionally equivalent with data mining in the sense used here.

By far, the trendiest term today is predictive analytics, which many companies ironically are using to differentiate what they do from "data mining." The inherent implication is that data mining is limited to the discovery of patterns, whereas predictive analytics allows the application of the patterns to new data to impute (or predict) unknown values. The motivation behind using the term predictive analytics is precisely this dilution of the meaning of data mining as it has been ...

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