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The Top Ten Algorithms in Data Mining

The Top Ten Algorithms in Data Mining

The Top Ten Algorithms in Data Mining
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The Top Ten Algorithms in Data Mining Hardback - 2009

by Wu, Xindong; Kumar, Vipin

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Details

  • Title The Top Ten Algorithms in Data Mining
  • Author Wu, Xindong; Kumar, Vipin
  • Binding Hardback
  • Condition New
  • Pages 230
  • Volumes 1
  • Language ENG
  • Publisher CRC Press
  • Publication date 2009-04-01
  • Illustrated Yes
  • Features Illustrated, Index, Table of Contents
  • Bookseller's Inventory # 5869093-n
  • ISBN 9781420089646 / 1420089641
  • Weight 0.95 lbs (0.43 kg)
  • Dimensions 9.3 x 6.4 x 0.7 in (23.62 x 16.26 x 1.78 cm)
  • Category Computers - Data Base Management
  • Library of Congress subjects Data mining, Computer algorithms
  • Library of Congress Catalogue Number 2009012819
  • Dewey Decimal Code 006.312
  • Quantity available 5

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From the publisher

Identifying some of the most influential algorithms that are widely used in the data mining community, The Top Ten Algorithms in Data Mining provides a description of each algorithm, discusses its impact, and reviews current and future research. Thoroughly evaluated by independent reviewers, each chapter focuses on a particular algorithm and is written by either the original authors of the algorithm or world-class researchers who have extensively studied the respective algorithm.

The book concentrates on the following important algorithms: C4.5, k-Means, SVM, Apriori, EM, PageRank, AdaBoost, kNN, Naive Bayes, and CART. Examples illustrate how each algorithm works and highlight its overall performance in a real-world application. The text covers key topics--including classification, clustering, statistical learning, association analysis, and link mining--in data mining research and development as well as in data mining, machine learning, and artificial intelligence courses.

By naming the leading algorithms in this field, this book encourages the use of data mining techniques in a broader realm of real-world applications. It should inspire more data mining researchers to further explore the impact and novel research issues of these algorithms.

Media reviews

Citations

  • Scitech Book News, 09/01/2009, Page 30

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