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Unsupervised Classification: Similarity Measures, Classical and Metaheuristic Approaches, and Applications 2013 ed. [Hardback]

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  • Formāts: Hardback, 262 pages, height x width: 235x155 mm, weight: 5443 g, XVIII, 262 p., 1 Hardback
  • Izdošanas datums: 12-Dec-2012
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642324509
  • ISBN-13: 9783642324505
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Unsupervised Classification: Similarity Measures, Classical and Metaheuristic Approaches, and Applications 2013 ed.Palielināt
  • Formāts: Hardback, 262 pages, height x width: 235x155 mm, weight: 5443 g, XVIII, 262 p., 1 Hardback
  • Izdošanas datums: 12-Dec-2012
  • Izdevniecība: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642324509
  • ISBN-13: 9783642324505
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9783642324505.html
Keywords:
Clustering is an important unsupervised classification technique where data points are grouped such that points that are similar in some sense belong to the same cluster. Cluster analysis is a complex problem as a variety of similarity and dissimilarity measures exist in the literature.This is the first book focused on clustering with a particular emphasis on symmetry-based measures of similarity and metaheuristic approaches. The aim is to find a suitable grouping of the input data set so that some criteria are optimized, and using this the authors frame the clustering problem as an optimization one where the objectives to be optimized may represent different characteristics such as compactness, symmetrical compactness, separation between clusters, or connectivity within a cluster. They explain the techniques in detail and outline many detailed applications in data mining, remote sensing and brain imaging, gene expression data analysis, and face detection.The book will be useful to graduate students and researchers in computer science, electrical engineering, system science, and information technology, both as a text and as a reference book. It will also be useful to researchers and practitioners in industry working on pattern recognition, data mining, soft computing, metaheuristics, bioinformatics, remote sensing, and brain imaging.

This book offers a theoretical analysis of symmetry-based clustering techniques. It includes extensive real-world applications in data mining, remote sensing imaging, MR brain imaging, gene expression data analysis, and face detection.

Recenzijas

From the reviews:

The book focuses on emerging metaheuristic approaches to unsupervised classification, with an emphasis on a symmetry-based definition of similarity. I found this book very appealing. I also thought of it as very valuable for my preoccupations towards the real-world application of unsupervised classification to medical imaging. I thus believe that, when reading this book, junior as well as experienced researchers will find many new challenging theoretical and practical ideas. (Catalin Stoean, zbMATH, Vol. 1276, 2014)

The book views clustering as a (multiobjective) optimization problem and tackles it with metaheuristics algorithms. More interestingly, the authors of this book propose the exploitation of the concepts of point and line symmetry to define new distances to be used in clustering techniques. researchers in the field will surely appreciate it as a good reference on the use of the symmetry notion in clustering. (Nicola Di Mauro, Computing Reviews, July, 2013)

1 Introduction
1(16)
1.1 Introduction
1(2)
1.2 Data Types with Examples
3(1)
1.3 Pattern Recognition: Preliminaries
4(7)
1.3.1 Data Acquisition
6(1)
1.3.2 Feature Selection
7(1)
1.3.3 Classification
7(1)
1.3.4 Clustering
8(1)
1.3.5 Distance Measures in Clustering
9(1)
1.3.6 Model Selection
10(1)
1.3.7 Model Order Selection
10(1)
1.4 Robustness to Outliers and Missing Values
11(1)
1.5 Fuzzy Set-Theoretic Approach: Relevance and Features
12(1)
1.6 Applications of Pattern Recognition and Learning
13(1)
1.7 Summary and Scope of the Book
14(3)
2 Some Single-and Multiobjective Optimization Techniques
17(42)
2.1 Introduction
17(1)
2.2 Single-Objective Optimization Techniques
18(7)
2.2.1 Overview of Genetic Algorithms
19(4)
2.2.2 Simulated Annealing
23(2)
2.3 Multiobjective Optimization
25(11)
2.3.1 Multiobjective Optimization Problems
26(2)
2.3.2 Various Methods to Solve MOOPs
28(1)
2.3.3 Recent Multiobjective Evolutionary Algorithms
29(4)
2.3.4 MOOPs and SA
33(3)
2.4 An Archive-Based Multiobjective Simulated Annealing Technique: AMOSA
36(10)
2.4.1 Introduction
36(1)
2.4.2 Archived Multiobjective Simulated Annealing (AMOSA)
37(1)
2.4.3 Archive Initialization
38(1)
2.4.4 Clustering the Archive Solutions
38(2)
2.4.5 Amount of Domination
40(1)
2.4.6 The Main AMOSA Process
41(4)
2.4.7 Complexity Analysis
45(1)
2.5 Simulation Results
46(11)
2.5.1 Comparison Measures
47(1)
2.5.2 Comparison of Binary-Encoded AMOSA with NSGA-II and PAES
48(3)
2.5.3 Comparison of Real-Coded AMOSA with the Algorithm of Smith et al. and Real-Coded NSGA-II
51(5)
2.5.4 Discussion on Annealing Schedule
56(1)
2.6 Discussion and Conclusions
57(2)
3 Similarity Measures
59(16)
3.1 Introduction
59(1)
3.2 Definitions
60(1)
3.2.1 Need for Measuring Similarity
60(1)
3.3 Similarity/Dissimilarity for Binary Variables
61(2)
3.4 Distance for Nominal/Categorical Variable
63(2)
3.4.1 Method 1: Each Category Is Represented by a Single Binary Variable [ 278]
64(1)
3.4.2 Method 2: Each Category Is Represented by Several Binary Variables [ 278]
64(1)
3.5 Distance for Ordinal Variables
65(3)
3.5.1 Normalized Rank Transformation
66(1)
3.5.2 Spearman Distance
67(1)
3.5.3 Footrule Distance
67(1)
3.6 Distance for Quantitative Variables
68(4)
3.6.1 Euclidean Distance
68(1)
3.6.2 Minkowski Distance of Order λ
68(1)
3.6.3 City Block Distance
69(1)
3.6.4 Chebyshev Distance
69(1)
3.6.5 Canberra Distance
70(1)
3.6.6 Bray-Curtis Distance
70(1)
3.6.7 Angular Separation
70(1)
3.6.8 Correlation Coefficient
71(1)
3.6.9 Mahalanobis Distance
72(1)
3.7 Normalization Methods
72(1)
3.8 Summary
73(2)
4 Clustering Algorithms
75(18)
4.1 Introduction
75(1)
4.2 Preliminaries
76(1)
4.2.1 Definition of Clustering
76(1)
4.2.2 Some Clustering Techniques
76(1)
4.3 Partitional Clustering Techniques
77(3)
4.3.1 K-Means Clustering Technique
77(2)
4.3.2 K-Medoid Clustering Technique
79(1)
4.3.3 Fuzzy C-Means Clustering Algorithm
79(1)
4.4 Distribution-Based Clustering Approach
80(2)
4.5 Hierarchical Clustering Techniques
82(1)
4.6 Density-Based Clustering Techniques
83(2)
4.7 Grid-Based Clustering Techniques
85(1)
4.8 Some Recent Clustering Techniques
85(2)
4.9 Some Evolutionary Approaches to Clustering
87(3)
4.9.1 Algorithms for a Fixed Value of the Number of Clusters
88(1)
4.9.2 Algorithms with Variable Number of Clusters
89(1)
4.10 MOO and Clustering
90(1)
4.11 Summary
91(2)
5 Point Symmetry-Based Distance Measures and Their Applications to Clustering
93(32)
5.1 Introduction
93(1)
5.2 Some Existing Symmetry-Based Distance Measures
94(6)
5.3 A New Definition of the Point Symmetry Distance [ 27]
100(2)
5.4 Some Properties of dps(x, c)
102(1)
5.5 Kd-Tree-Based Nearest Neighbor Computation
103(1)
5.6 GAPS: The Genetic Clustering Scheme with New PS-Distance [ 27]
104(6)
5.6.1 Chromosome Representation and Population Initialization
104(2)
5.6.2 Fitness Computation
106(1)
5.6.3 Selection
107(1)
5.6.4 Crossover
107(1)
5.6.5 Mutation
108(1)
5.6.6 Termination
109(1)
5.6.7 Complexity Analysis
109(1)
5.7 On the Convergence Property of GAPS
110(3)
5.7.1 Preliminaries
110(2)
5.7.2 Convergence Proof
112(1)
5.8 Experimental Results of GAPS
113(12)
5.8.1 Data Sets Used
113(1)
5.8.2 Implementation Results
114(7)
5.8.3 Summary
121(4)
6 A Validity Index Based on Symmetry: Application to Satellite Image Segmentation
125(40)
6.1 Introduction
125(1)
6.2 Some Existing Cluster Validity Indices
126(4)
6.2.1 BIC Index
126(1)
6.2.2 Calinski-Harabasz Index
127(1)
6.2.3 Silhouette Index
127(1)
6.2.4 DB Index
127(1)
6.2.5 Dunn's Index
128(1)
6.2.6 Xie-Beni Index
128(1)
6.2.7 PS Index
129(1)
6.2.8 I-Index
129(1)
6.2.9 CS-Index
129(1)
6.3 Sym-Index: The Symmetry-Based Cluster Validity Index
130(10)
6.3.1 The Cluster Validity Measure
130(4)
6.3.2 Mathematical Justification
134(2)
6.3.3 Interaction Between the Different Components of Sym-Index
136(4)
6.4 Experimental Results
140(7)
6.4.1 Data Sets
140(1)
6.4.2 Comparative Results
140(3)
6.4.3 Analysis of Results
143(4)
6.5 Incorporating dps in Some Existing Cluster Validity Indices
147(1)
6.6 Point Symmetry-Based Cluster Validity Indices
148(4)
6.6.1 Symmetry-Based Davies-Bouldin Index (Sym-DB Index)
148(1)
6.6.2 Symmetry-Based Dunn's Index (Sym-Dunn Index)
149(1)
6.6.3 Symmetry-Based Generalized Dunn's Index (Sym-GDunn Index)
149(1)
6.6.4 New Symmetry Distance-Based PS-Index (Sym-PS Index)
150(1)
6.6.5 Symmetry-Based Xie-Beni Index (Sym-XB Index)
151(1)
6.6.6 Symmetry-Based FS-Index (Sym-FS Index)
151(1)
6.6.7 Symmetry-Based K-Index (Sym-K Index)
151(1)
6.6.8 Symmetry-Based SV-Index (Sym-SV Index)
152(1)
6.7 Experimental Results
152(3)
6.7.1 Discussion of Results
152(3)
6.8 Application to Remote Sensing Imagery
155(5)
6.8.1 Simulated Circle Image (SCI)
156(1)
6.8.2 SPOT Image of Kolkata
156(4)
6.9 Discussion and Conclusions
160(5)
7 Symmetry-Based Automatic Clustering
165(32)
7.1 Introduction
165(1)
7.2 Some Existing Genetic Algorithm-Based Automatic Clustering Techniques
166(2)
7.3 Description of VGAPS
168(3)
7.3.1 Chromosome Representation and Population Initialization
168(1)
7.3.2 Fitness Computation
168(1)
7.3.3 Genetic Operations and Terminating Criterion
168(3)
7.4 On the Convergence Property of VGAPS
171(3)
7.4.1 To Check Whether the Mutation Matrix Is Positive
172(1)
7.4.2 Conditions on Selection
173(1)
7.5 Data Sets Used and Implementation Results
174(4)
7.5.1 Data Sets Used
174(1)
7.5.2 Results and Discussions
174(4)
7.6 Extending VGAPS to Fuzzy Clustering
178(4)
7.6.1 Fuzzy Symmetry-Based Cluster Validity Index
178(1)
7.6.2 Fuzzy-VGAPS Clustering
179(3)
7.7 Implementation Results and Comparative Study
182(12)
7.7.1 Discussion of Results
184(5)
7.7.2 Application to MR Brain Image Segmentation
189(1)
7.7.3 Experimental Results
190(4)
7.8 Summary
194(3)
8 Some Line Symmetry Distance-Based Clustering Techniques
197(20)
8.1 Introduction
197(1)
8.2 The Line Symmetry-Based Distance
197(2)
8.2.1 Definition
198(1)
8.3 GALSD: The Genetic Clustering Scheme with Line Symmetry-Based Distance
199(1)
8.3.1 String Representation and Population Initialization
199(1)
8.3.2 Fitness Computation
199(1)
8.3.3 Genetic Operators
200(1)
8.4 Experimental Results
200(4)
8.4.1 Data Sets Used
200(2)
8.4.2 Discussion of Results
202(1)
8.4.3 Computation Time
203(1)
8.5 Application to Face Recognition
204(5)
8.5.1 Human Face Detection Algorithm
205(2)
8.5.2 Experimental Results
207(2)
8.6 A Generalized Line Symmetry-Based Distance and Its Application to Data Clustering
209(1)
8.7 Implementation Results
210(5)
8.7.1 Data Sets Used
211(1)
8.7.2 Discussion of Results
211(4)
8.8 Discussion and Conclusions
215(2)
9 Use of Multiobjective Optimization for Data Clustering
217(28)
9.1 Introduction
217(2)
9.2 MOPS: Multiobjective Clustering Using Point Symmetry Distance
219(2)
9.2.1 Selection of a Solution from the Archive
220(1)
9.2.2 Experimental Results
220(1)
9.3 VAMOSA: Symmetry-Based Multiobjective Clustering Technique for Automatic Evolution of Clusters
221(6)
9.3.1 Data Sets Used for Experiment
222(1)
9.3.2 Experimental Results
223(4)
9.4 A Generalized Automatic Clustering Algorithm in a Multiobjective Framework
227(6)
9.4.1 GenClustMOO: Multiobjective Clustering Technique
228(4)
9.4.2 Subcluster Merging for Objective Function Calculation
232(1)
9.5 Experimental Results
233(9)
9.5.1 Data Sets Used
233(2)
9.5.2 Discussion of Results
235(7)
9.6 Discussion and Conclusions
242(3)
References 245(14)
Index 259
Prof. Sanghamitra Bandyopadhyay has many years of experience in the development of soft computing techniques. Among other awards and positions, she has received senior researcher Humboldt Fellowships, and she is a regular visitor to the DKFZ (German Cancer Research Centre) and to European and North American universities, collaborating in multidisciplinary teams on applications in the areas of computational biology and bioinformatics. Among other awards Prof. Bandyopadhyay received the prestigious Shanti Swarup Bhatnagar Prize in Engineering Sciences in 2010, she is a Fellow of the National Academy of Sciences of India and she is a Fellow of the Indian National Academy of Engineering. Dr. Sriparna Saha is an assistant professor in the Indian Institute of Technology Patna. Among her positions and awards, she was a postdoctoral researcher in Trento and in Heidelberg, and she received the Google India Women in Engineering Award in 2008. Her research interests include multiobjective optimization, evolutionary computation, clustering, and pattern recognition.