ABSTRACT
With the help of a hypothetical database containing customer information about credit card promotions, this chapter introduces several common data mining techniques. It provides a few basic tools to help one for the better understanding of the evaluation process. Data mining strategies include classification, estimation, prediction, unsupervised clustering, and market basket analysis. Classification and estimation strategies are similar in that each strategy is employed to build models able to generalize current outcome. However, the output of a classification strategy is categorical, whereas the output of an estimation strategy is numeric. A predictive strategy differs from a classification or estimation strategy in that it is used to design models for predicting future outcome rather than current behavior. Unsupervised clustering strategies are employed to discover hidden concept structures in data as well as to locate atypical data instances. The purpose of market basket analysis is to find interesting relationships among entities such as retail products.
