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Bibliographic Details

Title: Pattern Recognition Algorithms for Data Mining: Scalability, Knowledge Discovery and Soft Granular Computing (Chapman & Hall/CRC Computer Science & Data Analysis)
Publisher: Chapman and Hall/CRC
Publication Date: 2004
Language: English
ISBN 10: 1584884576
ISBN 13: 9781584884576
Binding: Hardcover
Condition: Good

About this title

Synopsis

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

About the Author

Pal, Sankar K.; Mitra, Pabitra

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