Data Mining and Statistics for Decision Making
Stéphane Tufféry, Universitie of Paris-Dauphine, France

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives.

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.

Key Features:

• 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.