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Compression Schemes for Mining Large Datasets
Murty, M. Narasimha; Subrahmanya, S. V.
€ 65.98
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Description for Compression Schemes for Mining Large Datasets
Paperback. This book addresses the challenges of data abstraction generation using the 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. Series: Advances in Computer Vision and Pattern Recognition. Num Pages: 213 pages, 59 black & white illustrations, 3 colour illustrations, 66 black & white tables, biograph. BIC Classification: UNF; UYQ; UYQP. Category: (P) Professional & Vocational. Dimension: 235 x 155 x 12. Weight in Grams: 338.
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 ... Read more
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 ... Read more
Product Details
Format
Paperback
Publication date
2016
Publisher
Springer London Ltd United Kingdom
Number of pages
213
Condition
New
Series
Advances in Computer Vision and Pattern Recognition
Number of Pages
197
Place of Publication
England, United Kingdom
ISBN
9781447170556
SKU
V9781447170556
Shipping Time
Usually ships in 15 to 20 working days
Ref
99-15
About Murty, M. Narasimha; Subrahmanya, S. V.
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.
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