Data mining, as I understand the term, implies a process that is somewhat unfocused. It involves the application of algorithms to large quantities of data. Thousands, perhaps millions, of variable combinations are examined to discover (it is hoped) some specific patterns in the data that are useful. (These are often call "nuggets" of information, to extend the mining analogy.)

In this process, the analyst takes a neutral stance regarding where the useful patterns will be found. Instead of testing specific variable combinations, the entire data set is searched systematically, insuring that nothing is missed. Similarly, a miner might dig up a whole mountainside looking for gold, not knowing exactly where the nuggets will be found. (Fortunately, the analogy between data mining and real mining breaks down when considering the ecological impact.)

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