The top ten algorithms in data mining: [... the IEEE International Conference on Data Mining identified the top 10 algorithms in data mining for presentation at ICMD '06 in Hong Kong]

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, 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.

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.

Cover......Page 1 Title......Page 4 Copyright......Page 5 Contents......Page 6 Preface......Page 8 Acknowledgments......Page 10 About the Authors......Page 12 Contributors......Page 14 Chapter 1: C4.5......Page 16 Chapter 2: K-Means......Page 36 Chapter 3: SVM: Support Vector Machines......Page 52 Chapter 4: Apriori......Page 76 Chapter 5: EM......Page 108 Chapter 6: PageRank......Page 132 Chapter 7: AdaBoost......Page 142 Chapter 8: kNN: k-Nearest Neighbors......Page 166 Chapter 9: Naive Bayes......Page 178 Chapter 10: CART: Classification and Regression Trees......Page 194 Index......Page 218