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Compression Schemes for Mining Large Datasets A Machine Learning Perspective-[1]-[2013]-[pdf]-[T. Ravindra Babu, M. Narasimha Murty, S.V. Subrahmanya (auth.)]

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书籍信息:
书名: Compression Schemes for Mining Large Datasets: A Machine Learning Perspective
语言: English
格式: pdf
大小: 2.8M
页数: 197
年份: 2013
作者: T. Ravindra Babu, M. Narasimha Murty, S.V. Subrahmanya (auth.)
版次: 1
系列: Advances in Computer Vision and Pattern Recognition
出版社: Springer-Verlag London

简介

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.



目录
Front Matter....Pages I-XVI
Introduction....Pages 1-10
Data Mining Paradigms....Pages 11-46
Run-Length-Encoded Compression Scheme....Pages 47-66
Dimensionality Reduction by Subsequence Pruning....Pages 67-94
Data Compaction Through Simultaneous Selection of Prototypes and Features....Pages 95-124
Domain Knowledge-Based Compaction....Pages 125-145
Optimal Dimensionality Reduction....Pages 147-172
Big Data Abstraction Through Multiagent Systems....Pages 173-183
Back Matter....Pages 185-197

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