Frequent Pattern Mining
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Beschreibung
This comprehensive reference consists of 18 chapters from prominent researchers in the field. Each chapter is self-contained, and synthesizes one aspect of frequent pattern mining. An emphasis is placed on simplifying the content, so that students and practitioners can benefit from the book. Each chapter contains a survey describing key research on the topic, a case study and future directions. Key topics include: Pattern Growth Methods, Frequent Pattern Mining in Data Streams, Mining Graph Patterns, Big Data Frequent Pattern Mining, Algorithms for Data Clustering and more. Advanced-level students in computer science, researchers and practitioners from industry will find this book an invaluable reference.
Proposes numerous methods to solve some of the most fundamental problems in data mining and machine learning Presents various simplified perspectives, providing a range of information to benefit both students and practitioners Includes surveys on key research content, case studies and future research directions Includes supplementary material: sn.pub/extras
Autor*in
Charu C. Aggarwal
Themen in »Frequent Pattern Mining«
Association rules Data classification Data stream pattern mining Data streams Frequent pattern mining Frequent patterns Large itemsets Numerical data Privacy preserving methods Sequential pattern mining Sequential patterns Vertical data representation
Stimmen zu »Frequent Pattern Mining«
“This multiauthor volume offers a thorough review of methods in frequent pattern mining. … This volume will be an essential reference for both researchers and practitioners in data mining.” (H. Van Dyke Parunak, Computing Reviews, March, 2016)
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