
The Top Ten Algorithms in Data Mining
Description
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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
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