Provides all the fundamental algorithms for outlier analysis in great detail including those for advanced data types, including specific insights into when and why particular algorithms work effectively Discusses the latest ideas in the field such as outlier ensembles, matrix factorization, kernel methods, and neural networks Covers theoretical and practical aspects of outlier analysis including specific practical details for accurate implementation Offers numerous illustrations and exercises for classroom teaching, including a solution manual
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