This book looks at both classical and modern methods of data mining, such as clustering, discriminate analysis, decision trees, neural networks and support vector machines along with illustrative examples throughout the book to explain the theory of these models. Recent methods such as bagging and boosting, decision trees, neural networks, support vector machines and genetic algorithm are also discussed along with their advantages and disadvantages.
- Presents a comprehensive introduction to all techniques used in data mining and statistical learning.
- Includes coverage of data mining with R as well as a thorough comparison of the two industry leaders, SAS and SPSS.
- Gives practical tips for data mining implementation as well as the latest techniques and state of the art theory.
- Looks at a range of methods, tools and applications, such as scoring to web mining and text mining and presents their advantages and disadvantages.
- Supported by an accompanying website hosting datasets and user analysis.
Business intelligence analysts and statisticians, compliance and financial experts in both commercial and government organizations across all industry sectors will benefit from this book.