Discovering Knowledge in Data: An Introduction to Data Mining provides both practical experience and theoretical understanding needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real-world case studies, a step-by-step guides walk readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. The main topics include: * Data preprocessing and classification * Analysis * The decision tree * Neural and Kohonen networks * Hierarchical and k-means * Association rules * Model evaluation techniques Complete with dozens of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field strength to turn any data warehouse into actionable knowledge. An Instructor's Manual solution detailed presentation to all the problems in this book are available online.