Author name:
Soumen Chakrabarti,
Earl Cox,
Eibe Frank,
Ralf Hartmut Güting,
Jiawei Han,
Xia Jiang,
Micheline Kamber,
Sam S. Lightstone,
Thomas P. Nadeau,
Richard E Neapolitan,
Dorian Pyle,
Mamdouh Refaat,
Markus Schneider,
Toby J. Teorey,
Ian H. Witten
Regular marked price: $69.95Discount Price: $55.96
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Type of bind: Hardcover
Dewey Decimal Number: 005.74
EAN num: 9780123746290
ISBN number: 0123746299
Label: Morgan Kaufmann
Manufacturer: Morgan Kaufmann
Quantity: 1
Page Count: 496
Printing Date: November 21, 2008
Publishing house: Morgan Kaufmann
Sale Popularity Level: 126150
Studio: Morgan Kaufmann
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Editor's Notes and Comments:
Product Description:
This book brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases. It consolidates both introductory and advanced topics, thereby covering the gamut of data mining and machine learning tactics ? from data integration and pre-processing, to fundamental algorithms, to optimization techniques and web mining methodology.
The proposed book expertly combines the finest data mining material from the Morgan Kaufmann portfolio. Individual chapters are derived from a select group of MK books authored by the best and brightest in the field. These chapters are combined into one comprehensive volume in a way that allows it to be used as a reference work for those interested in new and developing aspects of data mining.
This book represents a quick and efficient way to unite valuable content from leading data mining experts, thereby creating a definitive, one-stop-shopping opportunity for customers to receive the information they would otherwise need to round up from separate sources.
* Chapters contributed by various recognized experts in the field let the reader remain up to date and fully informed from multiple viewpoints.
* Presents multiple methods of analysis and algorithmic problem-solving techniques, enhancing the reader's technical expertise and ability to implement practical solutions.
* Coverage of both theory and practice brings all of the elements of data mining together in a single volume, saving the reader the time and expense of making multiple purchases.
User popularity level:
Rated by buyers -
Part of the value of a book like this is to get relevant, well-focused pointers into the larger literature. Some chapters have "resources" sections; some "resources" sections have bibliographies. There is no consistent pattern to the coverage. Finally, and most damaging, many resources are simply missing: "Hartigan, 1975" is all you'll get to find out more about variations of k-means clustering.
I hope the publisher will rectify this by providing adequate bibliographical materials on their website or in later editions.
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