1st Edition

The Top Ten Algorithms in Data Mining

Edited By Xindong Wu, Vipin Kumar Copyright 2009
230 Pages 53 B/W Illustrations
by Chapman & Hall

208 Pages
by Chapman & Hall

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... Read more

C4.5, Naren Ramakrishnan

K-Means, Joydeep Ghosh and Alexander Liu

SVM: Support Vector Machines, Hui Xue, Qiang Yang, and Songcan Chen

Apriori, Hiroshi Motoda and Kouzou Ohara

EM, Geoffrey J. McLachlan and Shu-Kay Ng

PageRank, Bing Liu and Philip S. Yu

AdaBoost, Zhi-Hua Zhou and Yang Yu

kNN: k-Nearest Neighbors, Michael Steinbach and Pang-Ning Tan

Naïve Bayes, David J. Hand

CART: Classification and Regression Trees, Dan Steinberg

Index

Biography

Xindong Wu, Vipin Kumar

… 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