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This valuable text 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. Organized into eight chapters, the book begins by introducing PR, data mining, and knowledge discovery concepts. The authors proceed to analyze the tasks of multi-scale data condensation and dimensionality reduction. Then they explore the problem of learning with support vector machine (SVM), and conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.
Language
Place of publication
Publishing group
Target group
Professional and scholarly
Computer scientists, electrical engineers, statisticians, mathematicians, graduate students and researchers in system science, and information technology
Illustrations
54 s/w Abbildungen, 35 s/w Tabellen
54 b/w images, 35 tables and 87 equations
File size
ISBN-13
978-0-203-99807-6 (9780203998076)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
INTRODUCTION Introduction Pattern Recognition in Brief Knowledge Discovery in Databases (KDD) Data Mining Different Perspectives of Data Mining Scaling Pattern Recognition Algorithms to Large Data Sets Significance of Soft Computing in KDD Scope of the Book MULTISCALE DATA CONDENSATION Introduction Data Condensation Algorithms Multiscale Representation of Data Nearest Neighbor Density Estimate Multiscale Data Condensation Algorithm Experimental Results and Comparisons Summary UNSUPERVISED FEATURE SELECTION Introduction Feature Extraction Feature Selection Feature Selection Using Feature Similarity (FSFS) Feature Evaluation Indices Experimental Results and Comparisons Summary ACTIVE LEARNING USING SUPPORT VECTOR MACHINE Introduction Support Vector Machine Incremental Support Vector Learning with Multiple Points Statistical Query Model of Learning Learning Support Vectors with Statistical Queries Experimental Results and Comparison Summary ROUGH-FUZZY CASE GENERATION Introduction Soft Granular Computing Rough Sets Linguistic Representation of Patterns and Fuzzy Granulation Rough-fuzzy Case Generation Methodology Experimental Results and Comparison Summary ROUGH-FUZZY CLUSTERING Introduction Clustering Methodologies Algorithms for Clustering Large Data Sets CEMMiSTRI: Clustering using EM, Minimal Spanning Tree and Rough-fuzzy Initialization Experimental Results and Comparison Multispectral Image Segmentation Summary ROUGH SELF-ORGANIZING MAP Introduction Self-Organizing Maps (SOM) Incorporation of Rough Sets in SOM (RSOM) Rule Generation and Evaluation Experimental Results and Comparison Summary CLASSIFICATION, RULE GENERATION AND EVALUATION USING MODULAR ROUGH-FUZZY MLP Introduction Ensemble Classifiers Association Rules Classification Rules Rough-Fuzzy MLP Modular Evolution of Rough-Fuzzy MLP Rule Extraction and Quantitative Evaluation Experimental Results and Comparison Summary APPENDIX A: ROLE OF SOFT-COMPUTING TOOLS IN KDD Fuzzy Sets Neural Networks Neuro-Fuzzy Computing Genetic Algorithms Rough Sets Other Hybridizations APPENDIX B DATA SETS USED IN EXPERIMENTS