Frequent Pattern Mining
- 11h 12m
- Charu C. Aggarwal, Jiawei Han (eds)
- Springer
- 2014
- 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
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.
In this Book
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An Introduction to Frequent Pattern Mining
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Frequent Pattern Mining Algorithms—A Survey
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Pattern-Growth Methods
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Mining Long Patterns
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Interesting Patterns
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Negative Association Rules
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Constraint-Based Pattern Mining
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Mining and Using Sets of Patterns Through Compression
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Frequent Pattern Mining in Data Streams
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Big Data Frequent Pattern Mining
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Sequential Pattern Mining
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Spatiotemporal Pattern Mining—Algorithms and Applications
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Mining Graph Patterns
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Uncertain Frequent Pattern Mining
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Privacy Issues in Association Rule Mining
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Frequent Pattern Mining Algorithms for Data Clustering
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Supervised Pattern Mining and Applications to Classification
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Applications of Frequent Pattern Mining
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