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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.
List of contents
An Introduction to Frequent Pattern Mining.- Frequent Pattern Mining Algorithms: A Survey.- Pattern-growth Methods.- Mining Long Patterns.- Interesting Patterns.- Negative Association Rules.- Constraint-based Pattern Mining.- Mining and Using Sets of Patterns through Compression.- Frequent Pattern Mining in Data Streams.- Big Data Frequent Pattern Mining.- Sequential Pattern Mining.- Spatiotemporal Pattern Mining: Algorithms and Applications.- Mining Graph Patterns.- Uncertain Frequent Pattern Mining.- Privacy in Association Rule Mining.- Frequent Pattern Mining Algorithms for Data Clustering.- Supervised Pattern Mining and Applications to Classification.- Applications of Frequent Pattern Mining.
About the author
Jiawei Hanis Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign.
Summary
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.
Additional text
“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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"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)
