Téléchargez le livre : Compression Schemes for Mining Large Datasets

Compression Schemes for Mining Large Datasets

A Machine Learning Perspective de

, ,

Éditeur :

Springer


Collection :

Advances in Computer Vision and Pattern Recognition

Paru le : 2013-11-19

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Description
This book addresses the challenges of data abstraction generation using a least number of database scans, compressing data through novel lossy and non-lossy schemes, and carrying out clustering and classification directly in the compressed domain. Schemes are presented which are shown to be efficient both in terms of space and time, while simultaneously providing the same or better classification accuracy. Features: describes a non-lossy compression scheme based on run-length encoding of patterns with binary valued features; proposes a lossy compression scheme that recognizes a pattern as a sequence of features and identifying subsequences; examines whether the identification of prototypes and features can be achieved simultaneously through lossy compression and efficient clustering; discusses ways to make use of domain knowledge in generating abstraction; reviews optimal prototype selection using genetic algorithms; suggests possible ways of dealing with big data problems using multiagent systems.
Pages
197 pages
Collection
Advances in Computer Vision and Pattern Recognition
Parution
2013-11-19
Marque
Springer
EAN papier
9781447156062
EAN EPUB
9781447156079
Informations sur l'ebook
Nombre pages copiables
1
Nombre pages imprimables
19
Taille du fichier
1024 Ko
Prix
52,74 €

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