Includes bibliographical references and index.

Overview of data mining -- The development of a data mining study -- Data exploration and preparation -- Using commercial data -- Statistical and data mining software -- An outline of data mining methods -- Factor analysis -- Neural networks -- Cluster analysis -- Association analysis -- Classification and prediction methods -- An application of data mining: scoring -- Factors for success in a data mining project -- Text mining -- Web mining -- Appendix A: Elements of statistics -- Appendix B: further reading.

License restrictions may limit access.

"This practical guide to understanding and implementing data mining techniques discusses traditional methods--cluster analysis, factor analysis, linear regression, PLS regression, and generalized linear models--and recent methods--bagging and boosting, decision trees, neural networks, support vector machines, and genetic algorithm. The book focuses largely on credit scoring, one of the most common applications of predictive techniques, but also includes other descriptive techniques, such as customer segmentation. It also covers data mining with R, provides a comparison of SAS and SPSS, and includes an appendix presenting the necessary statistical background"-- Provided by publisher.

"Data Mining is a practical guide to understanding and implementing data mining techniques, featuring traditional methods such as cluster analysis, factor analysis, linear regression, PLS regression and generalised linear models"-- Provided by publisher.