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The Top Ten Algorithms In Data Mining The Ieee International Conference On Data Mining Identified The Top 10 Algorithms In Data Mining For Presentation At Icmd 06 In Hong Kong 1st Edition Wu

  • SKU: BELL-21354708
The Top Ten Algorithms In Data Mining The Ieee International Conference On Data Mining Identified The Top 10 Algorithms In Data Mining For Presentation At Icmd 06 In Hong Kong 1st Edition Wu
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The Top Ten Algorithms In Data Mining The Ieee International Conference On Data Mining Identified The Top 10 Algorithms In Data Mining For Presentation At Icmd 06 In Hong Kong 1st Edition Wu instant download after payment.

Publisher: Chapman & Hall/CRC Press
File Extension: PDF
File size: 2.47 MB
Author: Wu, Xindong(Editor);Kumar, Vipin(Editor)
ISBN: 9781420089646, 1420089641
Language: English
Year: 2009
Edition: 1

Product desciption

The Top Ten Algorithms In Data Mining The Ieee International Conference On Data Mining Identified The Top 10 Algorithms In Data Mining For Presentation At Icmd 06 In Hong Kong 1st Edition Wu by Wu, Xindong(editor);kumar, Vipin(editor) 9781420089646, 1420089641 instant download after payment.

Identifying some of the most influential algorithms that are widely used in the data mining community,The Top Ten Algorithms in Data Miningprovides 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.

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