Pattern recognition algorithms for data mining : scalability, knowledge discovery and soft granular computing

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Pattern recognition algorithms for data mining : scalability, knowledge discovery and soft granular computing / Sankar K. Pal and Pabitra Mitra

資料種別:

図書

出版情報:

Boca Raton, FL : Chapman & Hall/CRC, c2004

形態:

xxix, 244 p. ; 25 cm

著者名:

ISBN:

9781584884576 [1584884576]

書誌ID:

BA67615399

子書誌情報

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所蔵情報

他の版・巻

書誌詳細

注記:

Includes bibliographical references and index

主題:

Computer algorithms; Data mining; Granular computing; Pattern recognition systems

言語:

英語

目次情報:

Foreword

Preface

List of Tables

List of Figures

Introduction / 1：

Pattern Recognition in Brief / 1.1：

Data acquisition / 1.2.1：

Feature selection/extraction / 1.2.2：

Classification / 1.2.3：

Knowledge Discovery in Databases (KDD) / 1.3：

Data Mining / 1.4：

Data mining tasks / 1.4.1：

Data mining tools / 1.4.2：

Applications of data mining / 1.4.3：

Different Perspectives of Data Mining / 1.5：

Database perspective / 1.5.1：

Statistical perspective / 1.5.2：

Pattern recognition perspective / 1.5.3：

Research issues and challenges / 1.5.4：

Scaling Pattern Recognition Algorithms to Large Data Sets / 1.6：

Data reduction / 1.6.1：

Dimensionality reduction / 1.6.2：

Active learning / 1.6.3：

Data partitioning / 1.6.4：

Granular computing / 1.6.5：

Efficient search algorithms / 1.6.6：

Significance of Soft Computing in KDD / 1.7：

Scope of the Book / 1.8：

Multiscale Data Condensation / 2：

Data Condensation Algorithms / 2.1：

Condensed nearest neighbor rule / 2.2.1：

Learning vector quantization / 2.2.2：

Astrahan's density-based method / 2.2.3：

Multiscale Representation of Data / 2.3：

Nearest Neighbor Density Estimate / 2.4：

Multiscale Data Condensation Algorithm / 2.5：

Experimental Results and Comparisons / 2.6：

Density estimation / 2.6.1：

Test of statistical significance / 2.6.2：

Classification: Forest cover data / 2.6.3：

Clustering: Satellite image data / 2.6.4：

Rule generation: Census data / 2.6.5：

Study on scalability / 2.6.6：

Choice of scale parameter / 2.6.7：

Summary / 2.7：

Unsupervised Feature Selection / 3：

Feature Extraction / 3.1：

Feature Selection / 3.3：

Filter approach / 3.3.1：

Wrapper approach / 3.3.2：

Feature Selection Using Feature Similarity (FSFS) / 3.4：

Feature similarity measures / 3.4.1：

Feature selection through clustering / 3.4.2：

Feature Evaluation Indices / 3.5：

Supervised indices / 3.5.1：

Unsupervised indices / 3.5.2：

Representation entropy / 3.5.3：

Comparison: Classification and clustering performance / 3.6：

Redundancy reduction: Quantitative study / 3.6.2：

Effect of cluster size / 3.6.3：

Active Learning Using Support Vector Machine / 3.7：

Support Vector Machine / 4.1：

Incremental Support Vector Learning with Multiple Points / 4.3：

Statistical Query Model of Learning / 4.4：

Query strategy / 4.4.1：

Confidence factor of support vector set / 4.4.2：

Learning Support Vectors with Statistical Queries / 4.5：

Experimental Results and Comparison / 4.6：

Classification accuracy and training time / 4.6.1：

Effectiveness of the confidence factor / 4.6.2：

Margin distribution / 4.6.3：

Rough-fuzzy Case Generation / 4.7：

Soft Granular Computing / 5.1：

Rough Sets / 5.3：

Information systems / 5.3.1：

Indiscernibility and set approximation / 5.3.2：

Reducts / 5.3.3：

Dependency rule generation / 5.3.4：

Linguistic Representation of Patterns and Fuzzy Granulation / 5.4：

Rough-fuzzy Case Generation Methodology / 5.5：

Thresholding and rule generation / 5.5.1：

Mapping dependency rules to cases / 5.5.2：

Case retrieval / 5.5.3：

Rough-fuzzy Clustering / 5.6：

Clustering Methodologies / 6.1：

Algorithms for Clustering Large Data Sets / 6.3：

Clarans: Clustering large applications based upon randomized search / 6.3.1：

Birch: Balanced iterative reducing and clustering using hierarchies / 6.3.2：

Dbscan: Density-based spatial clustering of applications with noise / 6.3.3：

Sting: Statistical information grid / 6.3.4：

CemmiStri: Clustering using EM, Minimal Spanning Tree and Rough-fuzzy Initialization / 6.4：

Mixture model estimation via EM algorithm / 6.4.1：

Rough set initialization of mixture parameters / 6.4.2：

Mapping reducts to mixture parameters / 6.4.3：

Graph-theoretic clustering of Gaussian components / 6.4.4：

Multispectral Image Segmentation / 6.5：

Discretization of image bands / 6.6.1：

Integration of EM, MST and rough sets / 6.6.2：

Index for segmentation quality / 6.6.3：

Experimental results and comparison / 6.6.4：

Rough Self-Organizing Map / 6.7：

Self-Organizing Maps (SOM) / 7.1：

Learning / 7.2.1：

Effect of neighborhood / 7.2.2：

Incorporation of Rough Sets in SOM (RSOM) / 7.3：

Unsupervised rough set rule generation / 7.3.1：

Mapping rough set rules to network weights / 7.3.2：

Rule Generation and Evaluation / 7.4：

Extraction methodology / 7.4.1：

Evaluation indices / 7.4.2：

Clustering and quantization error / 7.5：

Performance of rules / 7.5.2：

Classification, Rule Generation and Evaluation using Modular Rough-fuzzy MLP / 7.6：

Ensemble Classifiers / 8.1：

Association Rules / 8.3：

Rule generation algorithms / 8.3.1：

Rule interestingness / 8.3.2：

Classification Rules / 8.4：

Rough-fuzzy MLP / 8.5：

Fuzzy MLP / 8.5.1：

Rough set knowledge encoding / 8.5.2：

Modular Evolution of Rough-fuzzy MLP / 8.6：

Algorithm / 8.6.1：

Evolutionary design / 8.6.2：

Rule Extraction and Quantitative Evaluation / 8.7：

Rule extraction methodology / 8.7.1：

Quantitative measures / 8.7.2：

Rule extraction / 8.8：

Role of Soft-Computing Tools in KDD / 8.9：

Fuzzy Sets / A.1：

Clustering / A.1.1：

Association rules / A.1.2：

Functional dependencies / A.1.3：

Data summarization / A.1.4：

Web application / A.1.5：

Image retrieval / A.1.6：

Neural Networks / A.2：

Clustering and self organization / A.2.1：

Regression / A.2.3：

Neuro-fuzzy Computing / A.3：

Genetic Algorithms / A.4：

Other Hybridizations / A.5：

Data Sets Used in Experiments / B：

References

Index

About the Authors

Foreword

Preface

List of Tables

List of Figures

Introduction / 1：

Pattern Recognition in Brief / 1.1：

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