Data Mining Methods and Applications

Abstract

In this chapter, we provide a review of the knowledge discovery process, including data handling, data mining methods and software, and current research activities. The introduction defines and provides a general background to data mining knowledge discovery in databases, following by an outline of the entire process in the second part. The third part presents data handling issues, including databases and preparation of the data for analysis. The fourth part, as the core of the chapter, describes popular data mining methods, separated as supervised versus unsupervised learning. Supervised learning methods are described in the context of both regression and classification, beginning with the simplest case of linear models, then presenting more complex modeling with trees, neural networks, and support vector machines, and concluding with some methods only for classification. Unsupervised learning methods are described under two categories: association rules and clustering. The fifth part presents past and current research projects, involving both industrial and business applications. Finally, the last part provides a brief discussion on remaining problems and future trends.