
The Top Ten Algorithms in Data Mining
Chapman & Hall/CRC (Publisher)
1st Edition
Published on 9. April 2009
Book
Hardback
230 pages
978-1-4200-8964-6 (ISBN)
Description
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.
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.
Reviews / Votes
... The text is easy to read as each chapter focuses on a particular algorithm and a consistent presentation style has been adopted throughout the book ... Each chapter was reviewed by two independent reviewers and one of the book editors-resulting in a text that will be a useful reference source for years to come.-International Statistical Review, 2010
If you are a quality professional looking for data analysis techniques beyond multiple regression, and you are comfortable reading high level mathematics, then this book may be for you.
-Journal of Quality Technology, Vol. 41, No. 4, October 2009
More details
Series
Language
English
Place of publication
Oxford
United Kingdom
Publishing group
Taylor & Francis Ltd
Target group
Professional and scholarly
Professional Practice & Development
Product notice
Paper over boards
Illustrations
53 s/w Abbildungen, 26 s/w Tabellen
26 Tables, black and white; 53 Illustrations, black and white
Dimensions
Height: 240 mm
Width: 161 mm
Thickness: 17 mm
Weight
514 gr
ISBN-13
978-1-4200-8964-6 (9781420089646)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions

Xindong Wu | Vipin Kumar
The Top Ten Algorithms in Data Mining
E-Book
04/2009
1st Edition
Chapman & Hall/CRC
€138.99
Available for download

Xindong Wu | Vipin Kumar
The Top Ten Algorithms in Data Mining
E-Book
04/2009
Chapman and Hall
€138.99
Available for download
Persons
University of Vermont, Burlington, USA University of Minnesota, Minneapolis, USA
Content
C4.5. K-Means. SVM: Support Vector Machines. A priori. EM. PageRank. AdaBoost. kNN: k-Nearest Neighbors. Naive Bayes. CART: Classification and Regression Trees. Index.