Compression Schemes for Mining Large Datasets: A Machine Learning Perspective - Couverture rigide

Ravindra Babu, T.; Narasimha Murty, M.; Subrahmanya, S.V.

Couverture rigide

ISBN 10 : 1447156064 ISBN 13 : 9781447156062

Editeur : Springer London Ltd, 2013

Synopsis

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.

A propos du livre

À propos de l?auteur

Dr. T. Ravindra Babu is a Principal Researcher in the E-Commerce Research Labs at Infosys Ltd., Bangalore, India. Mr. S.V. Subrahmanya is Vice President and Research Fellow at the same organization. Dr. M. Narasimha Murty is a Professor in the Department of Computer Science and Automation at the Indian Institute of Science, Bangalore, India.

Détails de l'édition

• Éditeur: Springer London Ltd
• Date d'édition: 2013
• Langue: anglais
• ISBN 10: 1447156064
• ISBN 13: 9781447156062
• Reliure: Relié
• Nombre de pages: 213
• Coordonnées du fabricant: ProductSafety@springernature.com; ProductSafety@springernature.com; ; Poststr. 9; Darmstadt; 64293; Allemagne