Discovering Knowledge in Data: An Introduction to Data Mining by Daniel T. Larose (Author)

Discovering Knowledge in Data: An Introduction to Data Mining by Daniel T. Larose (Author)
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Discovering Knowledge in Data: An Introduction to Data Mining

by Daniel T. Larose (Author)

Publisher: Wiley-Interscience; 1 edition (November 18, 2004)

Learn Data Mining by doing data mining 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.

An Instructor's Manual presenting detailed solutions to all the problems in the book is available online.



Review "...an excellent introductory book of data mining. I recommend it for every one who wants to learn data mining." (Journal of Statistical Software, May 2006) "...selected material is described in a simple, clear, and…precise way...case studies…examples, and screen shots has definitely added to the learning value of the book." (Journal of Biopharmaceutical Statistics, January/February 2006)

"...does a good job introducing data mining to novices...it skillfully previews some of the basic statistical issues needed to understand data mining techniques." (Journal of the American Statistical Association, December 2005)

"If you need a book to help colleagues understand your data mining procedures and results, this is the one you want to give them." (Technometrics, November 2005)

"…an excellent book…it should be useful for anyone interested in analysing epidemiological data." (Statistics in Medical Research, October 2005)

"...an excellent 'white-box' overview of established approaches for data analysis, in which readers are shown how, why, and when the methods work." (CHOICE, April 2005)

"Larose has the making of a good series of books on data mining…I, for one, look forward to the next two books in the series." (Computing Reviews.com, February 15, 2005)

From the Back Cover Learn Data Mining by doing data mining 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.