TY - BOOK ID - 46171956 TI - High-Utility Pattern Mining : Theory, Algorithms and Applications AU - Fournier-Viger, Philippe. AU - Lin, Jerry Chun-Wei. AU - Nkambou, Roger. AU - Vo, Bay. AU - Tseng, Vincent S. PY - 2019 SN - 3030049213 3030049205 PB - Cham : Springer International Publishing : Imprint: Springer, DB - UniCat KW - Data mining. KW - Engineering. KW - Artificial intelligence. KW - Computational Intelligence. KW - Artificial Intelligence. KW - Data Mining and Knowledge Discovery. KW - AI (Artificial intelligence) KW - Artificial thinking KW - Electronic brains KW - Intellectronics KW - Intelligence, Artificial KW - Intelligent machines KW - Machine intelligence KW - Thinking, Artificial KW - Bionics KW - Cognitive science KW - Digital computer simulation KW - Electronic data processing KW - Logic machines KW - Machine theory KW - Self-organizing systems KW - Simulation methods KW - Fifth generation computers KW - Neural computers KW - Construction KW - Industrial arts KW - Technology KW - Algorithmic knowledge discovery KW - Factual data analysis KW - KDD (Information retrieval) KW - Knowledge discovery in data KW - Knowledge discovery in databases KW - Mining, Data KW - Database searching KW - Computational intelligence. KW - Intelligence, Computational KW - Artificial intelligence KW - Soft computing UR - https://www.unicat.be/uniCat?func=search&query=sysid:46171956 AB - 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. . ER -