The Top Ten Algorithms in Data Mining

Xindong Wu

ISBN 10: 1420089641 / ISBN 13: 9781420089646

Published by Chapman And Hall/CRC, 2009

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Identifying some of the most influential algorithms that are widely used in the data mining community, this volume provides a description of each algorithm, discusses the impact of the algorithms, and reviews current and future research on the algorithms. 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.

Seller Inventory # 9781420089646

Bibliographic Details

• Title: The Top Ten Algorithms in Data Mining
• Publisher: Chapman And Hall/CRC
• Publication Date: 2009
• Language: English
• ISBN 10: 1420089641
• ISBN 13: 9781420089646
• Binding: Buch
• Condition: Neu
• Dimensions: 240W x 161H x 17D millimeters 240 millimeters width by 161 millimeters height by 17 millimeters depth
• Weight: 514 grams

About this title

Synopsis

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.

About the Author

Xindong Wu, Vipin Kumar