Overview
- Presents an overview of the theory and core methods used in utility mining
- Covers recent advances in high-utility mining
- Includes stream, incremental, sequence, and big data mining
- Discusses important applications and open-source software
Part of the book series: Studies in Big Data (SBD, volume 51)
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (12 chapters)
-
Front Matter
Keywords
About this book
This book presents an overview of techniques for discovering high-utility patterns (patterns with a high importance) in data. It introduces the main types of high-utility patterns, as well as the theory and core algorithms for high-utility pattern mining, and describes recent advances, applications, open-source software, and research opportunities. It also discusses several types of discrete data, including customer transaction data and sequential data.
The book consists of twelve chapters, seven of which are surveys presenting the main subfields of high-utility pattern mining, including itemset mining, sequential pattern mining, big data pattern mining, metaheuristic-based approaches, privacy-preserving pattern mining, and pattern visualization. The remaining five chapters describe key techniques and applications, such as discovering concise representations and regular patterns.
Reviews
“This book offers a comprehensive treatment of HUI mining. Researchers will find it invaluable not only for understanding the state of the art, but also for gaining new insights into additional research opportunities. … Academics, graduate students, and practitioners interested in HUI mining applications will find this book to be a great resource and can experiment with the algorithms using the SPMF open-source data mining software … .” (Raghvinder Sangwan, Computing Reviews, June 24, 2021)
Editors and Affiliations
Bibliographic Information
Book Title: High-Utility Pattern Mining
Book Subtitle: Theory, Algorithms and Applications
Editors: Philippe Fournier-Viger, Jerry Chun-Wei Lin, Roger Nkambou, Bay Vo, Vincent S. Tseng
Series Title: Studies in Big Data
DOI: https://doi.org/10.1007/978-3-030-04921-8
Publisher: Springer Cham
eBook Packages: Intelligent Technologies and Robotics, Intelligent Technologies and Robotics (R0)
Copyright Information: Springer Nature Switzerland AG 2019
Hardcover ISBN: 978-3-030-04920-1Published: 31 January 2019
eBook ISBN: 978-3-030-04921-8Published: 18 January 2019
Series ISSN: 2197-6503
Series E-ISSN: 2197-6511
Edition Number: 1
Number of Pages: VIII, 337
Number of Illustrations: 44 b/w illustrations, 79 illustrations in colour
Topics: Computational Intelligence, Artificial Intelligence, Data Mining and Knowledge Discovery