Pattern Recognition Algorithms for Data Mining

by Sankar K. Pal; Pabitra Mitra

• ISBN13: 9780203998076
• ISBN10: 0203998073
• Format: Nonspecific Binding
• Copyright: 2004-05-27
• Publisher: Taylor & Francis

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

Role of Soft-Computing Tools in Kdd

Fuzzy Sets

Neural Networks

Neuro-Fuzzy Computing

Genetic Algorithms

Rough Sets

Other Hybridizations

Data Sets Used in Experiments

Table of Contents provided by Publisher. All Rights Reserved.