UID:
almahu_9948043852002882
Format:
VIII, 337 p. 123 illus., 79 illus. in color.
,
online resource.
ISBN:
9783030049218
Series Statement:
Studies in Big Data, 51
Content:
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. .
Note:
Introduction -- Problem Definition -- Algorithms -- Extensions of the Problem -- Research Opportunities -- Open-Source Implementations -- Conclusion.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9783030049201
Additional Edition:
Printed edition: ISBN 9783030049225
Language:
English
DOI:
10.1007/978-3-030-04921-8
URL:
https://doi.org/10.1007/978-3-030-04921-8
URL:
Volltext
(URL des Erstveröffentlichers)
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