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.
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