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E-raamat: Pattern Recognition Algorithms for Data Mining

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, (Indian Institute of Technology, Kanpur, India)
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Pal (Indian Statistical Institute, Calcutta) and Mitra (computer science and engineering, Indian Institute of Technology, Kanpur) present a unified framework for addressing pattern recognition tasks which are essential for data mining, and provide detailed analyses of various methodologies for dealing with problems in which recognition plays an important role. In addition to classical methods, they look at new methodologies for data mining such as rough sets, rough fuzzy hybridization, granular computing, artificial neural networks, and genetic algorithms. Appendixes describe soft computing methodologies and supply data sets for experimentation. Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)

Pattern Recognition Algorithms for Data Mining covers the topic of data mining from a pattern recognition perspective. This unique book presents real life data sets from various domains, such as geographic information systems, remote sensing imagery, and population census, to demonstrate the use of innovative new methodologies. Classical approaches are covered along with granular computation by integrating fuzzy sets, artificial neural networks, and genetic algorithms for efficient knowledge discovery. The authors then compare the granular computing and rough fuzzy approaches with the more classical methods and clearly demonstrate why they are more efficient.

Arvustused

"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.

Foreword xiii
Preface xxi
List of Tables xxv
List of Figures xxvii
1 Introduction 1(28)
1.1 Introduction
1(2)
1.2 Pattern Recognition in Brief
3(4)
1.2.1 Data acquisition
4(1)
1.2.2 Feature selection/extraction
4(1)
1.2.3 Classification
5(2)
1.3 Knowledge Discovery in Databases (KDD)
7(3)
1.4 Data Mining
10(4)
1.4.1 Data mining tasks
10(2)
1.4.2 Data mining tools
12(1)
1.4.3 Applications of data mining
12(2)
1.5 Different Perspectives of Data Mining
14(3)
1.5.1 Database perspective
14(1)
1.5.2 Statistical perspective
15(1)
1.5.3 Pattern recognition perspective
15(1)
1.5.4 Research issues and challenges
16(1)
1.6 Scaling Pattern Recognition Algorithms to Large Data Sets
17(4)
1.6.1 Data reduction
17(1)
1.6.2 Dimensionality reduction
18(1)
1.6.3 Active learning
19(1)
1.6.4 Data partitioning
19(1)
1.6.5 Granular computing
20(1)
1.6.6 Efficient search algorithms
20(1)
1.7 Significance of Soft Computing in KDD
21(1)
1.8 Scope of the Book
22(7)
2 Multiscale Data Condensation 29(30)
2.1 Introduction
29(3)
2.2 Data Condensation Algorithms
32(2)
2.2.1 Condensed nearest neighbor rule
32(1)
2.2.2 Learning vector quantization
33(1)
2.2.3 Astrahan's density-based method
34(1)
2.3 Multiscale Representation of Data
34(3)
2.4 Nearest Neighbor Density Estimate
37(1)
2.5 Multiscale Data Condensation Algorithm
38(2)
2.6 Experimental Results and Comparisons
40(12)
2.6.1 Density estimation
41(1)
2.6.2 Test of statistical significance
41(6)
2.6.3 Classification: Forest cover data
47(1)
2.6.4 Clustering: Satellite image data
48(1)
2.6.5 Rule generation: Census data
49(3)
2.6.6 Study on scalability
52(1)
2.6.7 Choice of scale parameter
52(1)
2.7 Summary
52(7)
3 Unsupervised Feature Selection 59(24)
3.1 Introduction
59(1)
3.2 Feature Extraction
60(2)
3.3 Feature Selection
62(2)
3.3.1 Filter approach
63(1)
3.3.2 Wrapper approach
64(1)
3.4 Feature Selection Using Feature Similarity (FSFS)
64(7)
3.4.1 Feature similarity measures
65(3)
3.4.2 Feature selection through clustering
68(3)
3.5 Feature Evaluation Indices
71(3)
3.5.1 Supervised indices
71(1)
3.5.2 Unsupervised indices
72(1)
3.5.3 Representation entropy
73(1)
3.6 Experimental Results and Comparisons
74(8)
3.6.1 Comparison: Classification and clustering performance
74(5)
3.6.2 Redundancy reduction: Quantitative study
79(1)
3.6.3 Effect of cluster size
80(2)
3.7 Summary
82(1)
4 Active Learning Using Support Vector Machine 83(20)
4.1 Introduction
83(3)
4.2 Support Vector Machine
86(2)
4.3 Incremental Support Vector Learning with Multiple Points
88(1)
4.4 Statistical Query Model of Learning
89(2)
4.4.1 Query strategy
90(1)
4.4.2 Confidence factor of support vector set
90(1)
4.5 Learning Support Vectors with Statistical Queries
91(3)
4.6 Experimental Results and Comparison
94(7)
4.6.1 Classification accuracy and training time
94(3)
4.6.2 Effectiveness of the confidence factor
97(1)
4.6.3 Margin distribution
97(4)
4.7 Summary
101(2)
5 Rough-fuzzy Case Generation 103(20)
5.1 Introduction
103(2)
5.2 Soft Granular Computing
105(1)
5.3 Rough Sets
106(5)
5.3.1 Information systems
107(1)
5.3.2 Indiscernibility and set approximation
107(1)
5.3.3 Reducts
108(2)
5.3.4 Dependency rule generation
110(1)
5.4 Linguistic Representation of Patterns and Fuzzy Granulation
111(3)
5.5 Rough-fuzzy Case Generation Methodology
114(6)
5.5.1 Thresholding and rule generation
115(2)
5.5.2 Mapping dependency rules to cases
117(1)
5.5.3 Case retrieval
118(2)
5.6 Experimental Results and Comparison
120(1)
5.7 Summary
121(2)
6 Rough-fuzzy Clustering 123(26)
6.1 Introduction
123(1)
6.2 Clustering Methodologies
124(2)
6.3 Algorithms for Clustering Large Data Sets
126(3)
6.3.1 CLARANS: Clustering large applications based upon randomized search
126(1)
6.3.2 BIRCH: Balanced iterative reducing and clustering using hierarchies
126(1)
6.3.3 DBSCAN: Density-based spatial clustering of applications with noise
127(1)
6.3.4 STING: Statistical information grid
128(1)
6.4 CEMMiSTRI: Clustering using EM, Minimal Spanning Tree and Rough-fuzzy Initialization
129(6)
6.4.1 Mixture model estimation via EM algorithm
130(1)
6.4.2 Rough set initialization of mixture parameters
131(1)
6.4.3 Mapping reducts to mixture parameters
132(1)
6.4.4 Graph-theoretic clustering of Gaussian components
133(2)
6.5 Experimental Results and Comparison
135(4)
6.6 Multispectral Image Segmentation
139(8)
6.6.1 Discretization of image bands
141(1)
6.6.2 Integration of EM, MST and rough sets
141(1)
6.6.3 Index for segmentation quality
141(1)
6.6.4 Experimental results and comparison
141(6)
6.7 Summary
147(2)
7 Rough Self-Organizing Map 149(16)
7.1 Introduction
149(1)
7.2 Self-Organizing Maps (SOM)
150(2)
7.2.1 Learning
151(1)
7.2.2 Effect of neighborhood
152(1)
7.3 Incorporation of Rough Sets in SOM (RSOM)
152(2)
7.3.1 Unsupervised rough set rule generation
153(1)
7.3.2 Mapping rough set rules to network weights
153(1)
7.4 Rule Generation and Evaluation
154(2)
7.4.1 Extraction methodology
154(1)
7.4.2 Evaluation indices
155(1)
7.5 Experimental Results and Comparison
156(7)
7.5.1 Clustering and quantization error
157(5)
7.5.2 Performance of rules
162(1)
7.6 Summary
163(2)
8 Classification, Rule Generation and Evaluation using Modular Rough-fuzzy MLP 165(36)
8.1 Introduction
165(2)
8.2 Ensemble Classifiers
167(3)
8.3 Association Rules
170(3)
8.3.1 Rule generation algorithms
170(3)
8.3.2 Rule interestingness
173(1)
8.4 Classification Rules
173(2)
8.5 Rough-fuzzy MLP
175(3)
8.5.1 Fuzzy MLP
175(1)
8.5.2 Rough set knowledge encoding
176(2)
8.6 Modular Evolution of Rough-fuzzy MLP
178(6)
8.6.1 Algorithm
178(4)
8.6.2 Evolutionary design
182(2)
8.7 Rule Extraction and Quantitative Evaluation
184(5)
8.7.1 Rule extraction methodology
184(4)
8.7.2 Quantitative measures
188(1)
8.8 Experimental Results and Comparison
189(10)
8.8.1 Classification
190(2)
8.8.2 Rule extraction
192(7)
8.9 Summary
199(2)
A Role of Soft-Computing Tools in KDD 201(10)
A.1 Fuzzy Sets
201(5)
A.1.1 Clustering
202(1)
A.1.2 Association rules
203(1)
A.1.3 Functional dependencies
204(1)
A.1.4 Data summarization
204(1)
A.1.5 Web application
205(1)
A.1.6 Image retrieval
205(1)
A.2 Neural Networks
206(1)
A.2.1 Rule extraction
206(1)
A.2.2 Clustering and self organization
206(1)
A.2.3 Regression
207(1)
A.3 Neuro-fuzzy Computing
207(1)
A.4 Genetic Algorithms
208(1)
A.5 Rough Sets
209(1)
A.6 Other Hybridizations
210(1)
B Data Sets Used in Experiments 211(4)
References 215(22)
Index 237(6)
About the Authors 243


Pal, Sankar K.; Mitra, Pabitra
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