The Top Ten Algorithms in Data Mining

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.

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