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

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019
• Edizione: 1st ed. 2019.
• Descrizione fisica: 1 online resource (343 pages)
• Disciplina: 006.312
• Soggetto topico: Computational intelligence; Artificial intelligence; Data mining; Computational Intelligence; Artificial Intelligence; Data Mining and Knowledge Discovery
• Persona (resp. second.): Fournier-VigerPhilippe; LinJerry Chun-Wei; NkambouRoger; VoBay; TsengVincent S
• Nota di contenuto: Introduction -- Problem Definition -- Algorithms -- Extensions of the Problem -- Research Opportunities -- Open-Source Implementations -- Conclusion.
• Sommario/riassunto: 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. .
• Titolo autorizzato: High-Utility Pattern Mining
• ISBN: 3-030-04921-3
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910739480503321
• Lo trovi qui: Univ. Federico II
• Serie: Studies in Big Data, . 2197-6503 ; ; 51