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Discovering knowledge in data : an introduction to data mining

Author: Daniel T Larose
Publisher: Hoboken, N.J. : Wiley-Interscience, ©2005.
Edition/Format:   eBook : Document : EnglishView all editions and formats
Summary:
Annotation Learn Data Mining by doing data miningData 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  Read more...
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Genre/Form: Electronic books
Additional Physical Format: Print version:
Larose, Daniel T.
Discovering knowledge in data.
Hoboken, N.J. : Wiley-Interscience, ©2005
(DLC) 2004003680
(OCoLC)54460464
Material Type: Document, Internet resource
Document Type: Internet Resource, Computer File
All Authors / Contributors: Daniel T Larose
ISBN: 0471687537 9780471687535 0471687545 9780471687542 9780471666578 0471666572
OCLC Number: 57683277
Description: 1 online resource (xv, 222 pages) : illustrations
Contents: An introduction to data mining --
Data preprocessing --
Exploratory data analysis --
Statistical approaches to estimation and prediction --
k-nearest neighbor algorithm --
Decision trees --
Neural networks --
Hierarchical and k-means clustering --
Kohonen networks --
Association rules --
Model evaluation techniques.
Responsibility: Daniel T. Larose.
More information:

Abstract:

Annotation Learn Data Mining by doing data miningData 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 techniquesComplete 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.

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