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