High-Utility Pattern Mining, 1st ed. 2019 Theory, Algorithms and Applications Studies in Big Data Series, Vol. 51
Coordonnateurs : Fournier-Viger Philippe, Lin Jerry Chun-Wei, Nkambou Roger, Vo Bay, Tseng Vincent S.
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.
Presents an overview of the theory and core methods used in utility mining
Covers recent advances in high-utility mining
Includes stream, incremental, sequence, and big data mining
Discusses important applications and open-source software
Date de parution : 01-2019
Ouvrage de 337 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 105,49 €
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Mots-clés :
High Utility Pattern Mining; Pattern Mining; Big Data; Data Mining; High Utility Mining
