Pattern Recognition Algorithms for Data Mining

Pattern Recognition Algorithms for Data Mining

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Features

  • Introduces pattern recognition (PR) concepts and tasks including scalability to large data sets and knowledge discovery in databases (KDD)
  • Describes methodologies for multiscale data condensation and unsupervised dimentionality reduction for large data
  • Presents active learning strategies for handling a large quadratic problem in an SVM framework
  • Deploys a rough-fuzzy framework for case generation and for clustering large data sets
  • Describes design procedure of a rough self-organizing map (RSOM)
  • Applies fuzzy sets, rough sets, neural nets, and genetic algorithms for problems of classification, rule generation, and evaluation in a supervised mode
  • Provides experimental results on real life data along with their sources
  • Summary

    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.

    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 EXPERIMENTS

    Editorial Reviews

    "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, USA.