Knowledge Discovery and Data Mining

The term data mining evokes an image of the old-time panner for gold—sifting through mounds of dirt trying to find those elusive valuable nuggets that make the whole process worthwhile. The translation into the information world is the data analyst sifting through terabytes of data looking for the corresponding knowledge nugget. This image is so powerful that the original meaning of data mining is lost in the media hype that surrounds information exploitation. Any information worker with a query tool connected to a database running ad hoc queries is called a “data miner.” As an alternative to proactive business intelligence (BI) operations, the knowledge discovery process is a means for finding new intelligence from collections of data. The term “data mining” has become overloaded, so more a correct term “knowledge discovery” is used instead. Knowledge discovery refers to the process of discovering patterns that lead to actionable knowledge from large data sets through one or more traditional data mining techniques, such as market basket analysis and clustering.