High-Utility Pattern Mining
Theory, Algorithms and Applications
Herausgeber: Fournier-Viger, P., Lin, J.C.-W., Nkambou, R., Vo, B., Tseng, V. (Eds.)
Vorschau- 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
Dieses Buch kaufen
- Über dieses Buch
-
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.
- Inhaltsverzeichnis (12 Kapitel)
-
-
A Survey of High Utility Itemset Mining
Seiten 1-45
-
A Comparative Study of Top-K High Utility Itemset Mining Methods
Seiten 47-74
-
A Survey of High Utility Pattern Mining Algorithms for Big Data
Seiten 75-96
-
A Survey of High Utility Sequential Pattern Mining
Seiten 97-129
-
Efficient Algorithms for High Utility Itemset Mining Without Candidate Generation
Seiten 131-160
-
Inhaltsverzeichnis (12 Kapitel)
Dieses Buch kaufen
Services zu diesem Buch
Wir empfehlen
Bibliografische Information
- Bibliographic Information
-
- Buchtitel
- High-Utility Pattern Mining
- Buchuntertitel
- Theory, Algorithms and Applications
- Herausgeber
-
- Philippe Fournier-Viger
- Jerry Chun-Wei Lin
- Roger Nkambou
- Bay Vo
- Vincent Tseng
- Titel der Buchreihe
- Studies in Big Data
- Buchreihen Band
- 51
- Copyright
- 2019
- Verlag
- Springer International Publishing
- Copyright Inhaber
- Springer Nature Switzerland AG
- eBook ISBN
- 978-3-030-04921-8
- DOI
- 10.1007/978-3-030-04921-8
- Hardcover ISBN
- 978-3-030-04920-1
- Buchreihen ISSN
- 2197-6503
- Auflage
- 1
- Seitenzahl
- VIII, 337
- Anzahl der Bilder
- 44 schwarz-weiß Abbildungen, 79 Abbildungen in Farbe
- Themen
*sofort verfügbar bei Bestellung eines Print-Titels, da es aufgrund der COVID-19-Situation zu Lieferverzögerungen kommen kann. Dieses Angebot ist befristet und richtet sich nach der Verfügbarkeit des eBook-Titels. Das eBook geht nicht in Ihren Besitz über - Sie erhalten einen zeitlich begrenzten Zugriff. Springer Reference Works sind davon ausgenommen.
