High-utility pattern mining [electronic resource] : theory, algorithms and applications / edited by Philippe Fournier-Viger ... [et al.].
- 其他作者：
- 其他題名：
- Studies in big data ;
- 出版： Cham : Springer International Publishing :Imprint: Springer 2019.
- 叢書名： Studies in big data,v.51
- 主題： Data mining. , Big data. , Computational Intelligence. , Artificial Intelligence. , Data Mining and Knowledge Discovery.
- ISBN： 9783030049218 (electronic bk.) 、 9783030049201 (paper)
- URL：
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電子書
- 一般註：Introduction -- Problem Definition -- Algorithms -- Extensions of the Problem -- Research Opportunities -- Open-Source Implementations -- Conclusion. E1084學校採購電子書
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讀者標籤：
- 系統號： 000274328 | 機讀編目格式
館藏資訊
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.
摘要註
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.