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High-Utility Pattern Mining : Theory, Algorithms and Applications / edited by Philippe Fournier-Viger, Jerry Chun-Wei Lin, Roger Nkambou, Bay Vo, Vincent S. Tseng

Contributor(s): Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Studies in Big Data ; 51 | SpringerLink BücherPublisher: Cham : Springer International Publishing, 2019Description: Online-Ressource (VIII, 337 p. 123 illus., 79 illus. in color, online resource)ISBN:
  • 9783030049218
Subject(s): Additional physical formats: 9783030049201 | 9783030049225 | Erscheint auch als: 978-3-030-04920-1 Druck-Ausgabe | Printed edition: 9783030049201 | Printed edition: 9783030049225 DDC classification:
  • 006.3
LOC classification:
  • Q342
DOI: DOI: 10.1007/978-3-030-04921-8Online resources: Summary: 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 patternsSummary: Introduction -- Problem Definition -- Algorithms -- Extensions of the Problem -- Research Opportunities -- Open-Source Implementations -- ConclusionPPN: PPN: 1048369307Package identifier: Produktsigel: ZDB-2-INR | ZDB-2-SEB | ZDB-2-SXIT
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