Pattern Recognition Algorithms for Data Mining: 1st Edition (Hardback) book cover

Pattern Recognition Algorithms for Data Mining

1st Edition

By Sankar K. Pal, Pabitra Mitra

Chapman and Hall/CRC

280 pages | 54 B/W Illus.

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Hardback: 9781584884576
pub: 2004-05-27
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Description

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.

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.

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

About the Series

Chapman & Hall/CRC Computer Science & Data Analysis

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Subject Categories

BISAC Subject Codes/Headings:
COM000000
COMPUTERS / General
COM021030
COMPUTERS / Database Management / Data Mining
COM051300
COMPUTERS / Programming / Algorithms