The Top Ten Algorithms in Data Mining

by Xindong Wu (Editor), Vipin Kumar (Editor)

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 C4.5, k -Means, SVM, Apriori, EM, PageRank, AdaBoost, k NN, 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.

230 pages, Hardcover

First published April 1, 2009

Reviews

[Topilno]

Creating Web Pages for Dummies

[Jacek · Rating 5 out of 5]

Great introduction to the subject for a reader with good mathematical background looking for theoretical foundations of data mining methods as well as for their practical side.