Pattern Recognition Algorithms for Data Mining
Features
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.
