Discovering Knowledge in Data

The field of data mining lies at the confluence of statistical analysis, machine learning and business intelligence. However, nearly all the books published so far have been restricted solely to a single one of these areas, usually either machine learning or business intelligence.

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Learn Data Mining by doing data mining Data mining can be revolutionary--but only when ita s done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets. Employing a "white box" methodology and with real--world case studies, this step--by--step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets.
Principal topics include: aeo Data preprocessing and classification aeo Exploratory analysis aeo Decision trees aeo Neural and Kohonen networks aeo Hierarchical and k--means clustering aeo Association rules aeo Model evaluation techniques Complete with scores 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 the power to turn any data warehouse into actionable knowledge. An Instructora s Manual presenting detailed solutions to all the problems in the book is available online.