ABSTRACT

Part Π, consisting of four chapters, describes techniques and tools for data mining. In Chapter 6 we start with a discussion of the steps for data mining. These steps include preparing the data, selecting the tools, carrying out the mining, post processing of the results, and taking actions and measuring success. Then in Chapter 7 we discuss outcomes and techniques for data mining. Data mining outcomes include classification, clustering, estimation, prediction, and associations/correlations. The outcomes are what can be expected as a result of data mining. Data mining techniques are the algorithms employed for doing the mining. These include neural networks, decision trees, and genetic algorithms. We pay special attention to a particular data mining technique based on inductive logic programming. This is mainly due to our interest in this topic. Inductive logic programming and data mining is the subject of Chapter 8. Then in Chapter 9 we provide an overview of the various prototypes and commercial tools in data mining. It should be noted that due to the rapid advances, the information on prototypes and products could soon be outdated. Therefore we encourage the reader to keep up with product information.