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High-Utility Pattern Mining

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High-Utility Pattern Mining Synopsis

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.

 

About This Edition

ISBN: 9783030049201
Publication date: 31st January 2019
Author: Philippe FournierViger, Jerry ChunWei Lin, Roger Nkambou, Bay Vo, Vincent S Tseng
Publisher: Springer an imprint of Springer International Publishing
Format: Hardback
Pagination: 337 pages
Series: Studies in Big Data
Genres: Artificial intelligence
Expert systems / knowledge-based systems
Data mining