Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from statistics and pattern recognition.
A simple example of data mining is its use in a retail sales department. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk shirts, the data mining system will make a correlation between that customer and silk shirts. The sales department will look at that information and may begin direct mail marketing of silk shirts to that customer, or it may alternatively attempt to get the customer to buy a wider range of products. In this case, the data mining system used by the retail store discovered new information about the customer that was previously unknown to the company.
Data Dredging
Used in the technical context of data warehousing and analysis, the term "data mining" is neutral. However, it sometimes has a more pejorative usage that implies imposing patterns (and particularly causal relationships) on data where none exist. This imposition of irrelevant, misleading or trivial attribute correlation is more properly criticized as "data dredging" in the statistical literature.
Used in this latter sense, data dredging implies scanning the data for any relationships, and then when one is found coming up with an interesting explanation. (This is also referred to as "overfitting the model".) The problem is that large data sets invariably happen to have some exciting relationships peculiar to that data. Therefore any conclusions reached are likely to be highly suspect. In spite of this, some exploratory data work is always required in any applied statistical analysis to get a feel for the data, so sometimes the line between good statistical practice and data dredging is less than clear.
via [ Data Blog ]
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