Modern corporations collect massive amounts of data. Citibank alone, for instance, collects information on 3% of all the retail transactions in the United States. Even more information is available but is not collected. Of that which is collected, only a small percentage is used for tactical or strategic business purposes. There is simply too much to be examined by traditional methods. Hence the need for Data Mining.
Data Mining is the mathematical extraction of often difficult-to-detect patterns in these large data flows. Data Mining is an extension of, and is related to, traditional statistical methods. Unlike traditional statistics, however, modern Data Mining techniques are designed to extract information from large, continuous data flows common in modern business environments. Continuous data flows are never-ending and constantly changing. They are a reflection of the Information Age in which we live. Traditional statistical methods cannot handle the volume and rapid change. Data Mining techniques, on the other hand, have been designed to thrive in this milieu.
Because living organisms have to make sense of these types of data flows in order to survive, many of these new Data Mining methods are modeled after natural phenomena, such as natural selection and evolution, the neural structure of the brain, or the manner in which metals relax to their natural physical state after being heated.
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