Pattern Recognition Algorithms for Data Mining
Book Details
Format
Hardback or Cased Book
ISBN-10
1584884576
ISBN-13
9781584884576
Publisher
Taylor & Francis Inc
Imprint
Chapman & Hall/CRC
Country of Manufacture
GB
Country of Publication
GB
Publication Date
May 27th, 2004
Print length
274 Pages
Weight
542 grams
Dimensions
24.40 x 16.50 x 2.20 cms
Product Classification:
Machine learning
Ksh 26,100.00
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Addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Organized into eight chapters, the book begins by introducing PR, data mining, and knowledge discovery concepts. It concludes by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
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.
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.
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