High-Utility Pattern Mining. Theory, Algorithms and Applications.. Este artículo no está disponible.
Idioma: inglés
Editorial: Cham, Springer., 2019
- Tapa dura
- Usado
Librería: Universitätsbuchhandlung Herta Hold GmbH, Berlin, AlemaniaUniversitätsbuchhandlung Herta Hold GmbH
Vendedor de IberLibro desde 9 de abril de 2003
Condición: Usado
EUR 16,00
Descripción del artículo del vendedor
viii, 337 p. Hardcover. Versand aus Deutschland / We dispatch from Germany via Air Mail. Einband bestoßen, daher Mängelexemplar gestempelt, sonst sehr guter Zustand. Imperfect copy due to slightly bumped cover, apart from this in very good condition. Stamped. Sprache: Englisch.
N° de ref. del artículo 2967MB
- Título
- High-Utility Pattern Mining. Theory, Algorithms and Applications.
- Autor
- Fournier-Vigor, P. et al (Eds.)
- Editorial
- Cham, Springer.
- Año de publicación
- 2019
- Encuadernación
- Encuadernación de tapa dura
- Idioma
- inglés
- ISBN 10
- 3030049205
- ISBN 13
- 9783030049201
- Serie
- Libro 45 de 95: Studies in Big Data
- Catálogos de vendedores
- Technik
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.
“Sinopsis” puede pertenecer a otra edición de este título.
De la contraportada
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.
“Acerca de” puede pertenecer a otra edición de este título.