Includes bibliographical references and index.

Remote access database may be used by patrons with KCLS library card and pin.

"Data mining can be revolutionary- but only when it'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: Data preprocessing and classification; Exploratory analysis; Decision trees; Neural and Kohonen networks; Hierarchical and k-means clustering; Association rules; 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."--Page 4 of cover.

"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."--Jacket.

Available also in a print ed.

Mode of access: Internet via World Wide Web.

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