Data mining methodologies have been widely adopted in various business domains, such as database marketing, credit scoring, fraud detection, to name only a few of the areas where data mining has become an indispensable tool for business success. Increasingly data mining methods are also being applied to industrial process optimization and control. While the general approach is similar regardless of application (finding "nuggets" of new information in data), some specific methodologies and techniques for optimizing continuous processes, such as boiler performance in a coal-burning power plant, have proven particularly useful for those applications, and superior to existing traditional analytic approaches such as DOE (design of experiments), CFD (computational fluid dynamics), or statistical modeling. This paper will provide an introduction to data mining, and specifically contrast the methods used in data mining with traditional optimization techniques.
