Sometimes the purpose of data mining is simply to describe what is going on in a complicated database in a way that increases our understanding of the people, products, or processes that produced the data in the first place. A good enough description of a behavior will often suggest an explanation for it as well. At the very least, a good description suggests where to start looking for an explanation. The famous gender gap in American politics is an example of how a simple description, “women support Democrats in greater numbers than do men,” can provoke large amounts of interest and further study on the part of journalists, sociologists, economists, and political scientists, not to mention candidates for public office.
Most of the data mining techniques described in this book have existed, at least as academic algorithms, for years or decades. However, it is only in the last decade that commercial data mining has caught on in a big way. This is due to the convergence of several factors: The data is being produced.› The data is being warehoused.› Computing power is affordable.› Interest in customer relationship management is strong.› Commercial data mining software products are readily available.› Let’s look at each factor in turn.
PROFILING THE DATABASE TO UNDERSTAND BETTER THE CUSTOMERS