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The Top Ten Algorithms in Data Mining Xindong Wu (University of Louisiana at Lafayette)

The Top Ten Algorithms in Data Mining By Xindong Wu (University of Louisiana at Lafayette)

The Top Ten Algorithms in Data Mining by Xindong Wu (University of Louisiana at Lafayette)


Summary

Identifying some of the most influential algorithms that are widely used in the data mining community, this book provides a description of each algorithm, discusses the impact of the algorithms, and reviews research on the algorithms.

The Top Ten Algorithms in Data Mining Summary

The Top Ten Algorithms in Data Mining by Xindong Wu (University of Louisiana at Lafayette)

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 topicsincluding classification, clustering, statistical learning, association analysis, and link miningin 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 Top Ten Algorithms in Data Mining Reviews

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 editorsresulting 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

About Xindong Wu (University of Louisiana at Lafayette)

University of Vermont, Burlington, USA University of Minnesota, Minneapolis, USA

Table of Contents

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.

Additional information

GOR006538866
9781420089646
1420089641
The Top Ten Algorithms in Data Mining by Xindong Wu (University of Louisiana at Lafayette)
Used - Very Good
Hardback
Taylor & Francis Ltd
2009-04-09
230
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
This is a used book - there is no escaping the fact it has been read by someone else and it will show signs of wear and previous use. Overall we expect it to be in very good condition, but if you are not entirely satisfied please get in touch with us

Customer Reviews - The Top Ten Algorithms in Data Mining