High-Utility Pattern Mining

Lieferzeit: Lieferbar innerhalb 14 Tagen

106,99 

Theory, Algorithms and Applications, Studies in Big Data 51

ISBN: 3030049205
ISBN 13: 9783030049201
Herausgeber: Philippe Fournier-Viger/Jerry Chun-Wei Lin/Roger Nkambou et al
Verlag: Springer Verlag GmbH
Umfang: viii, 337 S., 44 s/w Illustr., 79 farbige Illustr., 337 p. 123 illus., 79 illus. in color.
Erscheinungsdatum: 31.01.2019
Auflage: 1/2019
Produktform: Gebunden/Hardback
Einband: GEB

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.

Artikelnummer: 5785235 Kategorie:

Beschreibung

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.

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