Pattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining : Scalability, Knowledge Discovery and Soft Granular Computing

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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 more

Product details

  • Hardback | 280 pages
  • 180.3 x 251.5 x 38.1mm | 1,179.35g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • New.
  • 54 black & white illustrations, 35 black & white tables
  • 1584884576
  • 9781584884576

Review quote

"Pattern Recognition Algorithms in Data Mining is a book that commands admiration. Its authors, Professors S.K. Pal and P. Mitra are foremost authorities in pattern recognition, data mining, and related fields. Within its covers, the reader finds an exceptionally well-organized exposition of every concept and every method that is of relevance to the theme of the book. There is much that is original and much that cannot be found in the literature. The authors and the publisher deserve our thanks and congratulations for producing a definitive work that contributes so much and in so many important ways to the advancement of both the theory and practice of recognition technology, data mining, and related fields. The magnum opus of Professors Pal and Mitra is must-reading for anyone who is interested in the conception, design, and utilization of intelligent systems." - from the Foreword by Lotfi A. Zadeh, University of California, Berkeley, USA "The book presents an unbeatable combination of theory and practice and provides a comprehensive view of methods and tools in modern KDD. The authors deserve the highest appreciation for this excellent monograph." - from the Foreword by Zdzislaw Pawlak, Polish Academy of Sciences, Warsaw" This volume provides a very useful, thorough exposition of the many facets of this application from several perspectives. I congratulate the authors of this volume and I am pleased to recommend it as a valuable addition to the books in this field." - from the Forword by Laveen N. Kanal, University of Maryland, College Park, more

Table of contents

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 EXPERIMENTSshow more

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