Muutke küpsiste eelistusi

Knowledge-Based Clustering: From Data to Information Granules [Kõva köide]

3.33/5 (6 hinnangut Goodreads-ist)
(University of Manitoba, Winnipeg, Canada)
  • Formaat: Hardback, 336 pages, kõrgus x laius x paksus: 242x160x20 mm, kaal: 603 g, Drawings: 0 B&W, 0 Color; Screen captures: 0 B&W, 0 Color
  • Ilmumisaeg: 18-Feb-2005
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0471469661
  • ISBN-13: 9780471469667
Teised raamatud teemal:
Knowledge-Based Clustering: From Data to Information GranulesSuurem pilt
  • Formaat: Hardback, 336 pages, kõrgus x laius x paksus: 242x160x20 mm, kaal: 603 g, Drawings: 0 B&W, 0 Color; Screen captures: 0 B&W, 0 Color
  • Ilmumisaeg: 18-Feb-2005
  • Kirjastus: Wiley-Interscience
  • ISBN-10: 0471469661
  • ISBN-13: 9780471469667
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9780471469667.html
Märksõnad:
Clustering, explains Pedrycz (electrical and computer engineering, U. of Alberta and systems research, Polish Academy of Sciences), is one of the well-established manifestations of a computer environment that helps people understand data and make sensible decisions. He introduces a knowledge-based version that is concerned with reconciling gaining data and domain knowledge, and with building a coherent platform of navigation in highly dimensional and often heterogenous data spaces. He keeps both the discussion and the algorithms fairly general, rather than optimizing them for any specific application, but does not attempt a comprehensive account of the current research. Annotation ©2004 Book News, Inc., Portland, OR (booknews.com)

  • A comprehensive coverage of emerging and current technology dealing with heterogeneous sources of information, including data, design hints, reinforcement signals from external datasets, and related topics
  • Covers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangible
  • Includes illustrative material andwell-known experimentsto offer hands-on experience

Arvustused

"I agree with Zadeh's opinion (mentioned at the end of book's foreword): 'The author and the publisher deserve our loud applause and congratulations.'" (Computing Reviews.com, May 19, 2005)

Foreword xiii
Preface xv
1 Clustering and Fuzzy Clustering
1(27)
1.1 Introduction
1(1)
1.2 Basic Notions and Notation
1(5)
1.2.1 Types of Data
2(1)
1.2.2 Distance and Similarity
2(4)
1.3 Main Categories of Clustering Algorithms
6(4)
1.3.1 Hierarchical Clustering
6(2)
1.3.2 Objective Function-Based Clustering
8(2)
1.4 Clustering and Classification
10(1)
1.5 Fuzzy Clustering
11(7)
1.6 Cluster Validity
18(1)
1.7 Extensions of Objective Function-Based Fuzzy Clustering
19(4)
1.7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy C Varieties
19(1)
1.7.2 Possibilistic Clustering
20(2)
1.7.3 Noise Clustering
22(1)
1.8 Self-Organizing Maps and Fuzzy Objective Function-Based Clustering
23(2)
1.9 Conclusions
25(1)
References
26(2)
2 Computing with Granular Information: Fuzzy Sets and Fuzzy Relations
28(22)
2.1 A Paradigm of Granular Computing: Information Granules and Their Processing
28(3)
2.2 Fuzzy Sets as Human-Centric Information Granules
31(1)
2.3 Operations on Fuzzy Sets
32(1)
2.4 Fuzzy Relations
33(2)
2.5 Comparison of Two Fuzzy Sets
35(2)
2.6 Generalizations of Fuzzy Sets
37(1)
2.7 Shadowed Sets
38(6)
2.8 Rough Sets
44(2)
2.9 Granular Computing and Distributed Processing
46(1)
2.10 Conclusions
47(1)
References
47(3)
3 Logic-Oriented Neurocomputing
50(16)
3.1 Introduction
50(1)
3.2 Main Categories of Fuzzy Neurons
51(8)
3.2.1 Aggregative Neurons
52(3)
3.2.2 Referential (Reference) Neurons
55(4)
3.3 Architectures of Logic Networks
59(2)
3.4 Interpretation Aspects of the Networks
61(1)
3.5 Granular Interfaces of Logic Processing
62(2)
3.6 Conclusions
64(1)
References
64(2)
4 Conditional Fuzzy Clustering
66(21)
4.1 Introduction
66(2)
4.2 Problem Statement: Context Fuzzy Sets and Objective Function
68(2)
4.3 The Optimization Problem
70(10)
4.4 Computational Considerations of Conditional Clustering
80(1)
4.5 Generalizations of the Algorithm Through the Aggregation Operator
81(1)
4.6 Fuzzy Clustering with Spatial Constraints
82(4)
4.7 Conclusions
86(1)
References
86(1)
5 Clustering with Partial Supervision
87(10)
5.1 Introduction
87(1)
5.2 Problem Formulation
88(2)
5.3 Design of the Clusters
90(1)
5.4 Experimental Examples
91(2)
5.5 Cluster-Based Tracking Problem
93(3)
5.6 Conclusions
96(1)
References
96(1)
6 Principles of Knowledge-Based Guidance in Fuzzy Clustering
97(32)
6.1 Introduction
97(2)
6.2 Examples of Knowledge-Oriented Hints and Their General Taxonomy
99(3)
6.3 The Optimization Environment of Knowledge-Enhanced Clustering
102(3)
6.4 Quantification of Knowledge-Based Guidance Hints and Their Optimization
105(2)
6.5 Organization of the Interaction Process
107(5)
6.6 Proximity-Based Clustering (P-FCM)
112(5)
6.7 Web Exploration and P-FCM
117(9)
6.8 Linguistic Augmentation of Knowledge-Based Hints
126(1)
6.9 Conclusions
127(1)
References
127(2)
7 Collaborative Clustering
129(29)
7.1 Introduction and Rationale
129(2)
7.2 Horizontal and Vertical Clustering
131(1)
7.3 Horizontal Collaborative Clustering
132(8)
7.3.1 Optimization Details
135(2)
7.3.2 The Flow of Computing of Collaborative Clustering
137(1)
7.3.3 Quantification of the Collaborative Phenomenon of Clustering
138(2)
7.4 Experimental Studies
140(10)
7.5 Further Enhancements of Horizontal Clustering
150(1)
7.6 The Algorithm of Vertical Clustering
151(2)
7.7 A Grid Model of Horizontal and Vertical Clustering
153(2)
7.8 Consensus Clustering
155(2)
7.9 Conclusions
157(1)
References
157(1)
8 Directional Clustering
158(20)
8.1 Introduction
158(1)
8.2 Problem Formulation
159(4)
8.2.1 The Objective Function
160(1)
8.2.2 The Logic Transformation Between Information Granules
161(2)
8.3 The Algorithm
163(3)
8.4 The Development Framework of Directional Clustering
166(1)
8.5 Numerical Studies
167(7)
8.6 Conclusions
174(2)
References
176(2)
9 Fuzzy Relational Clustering
178(13)
9.1 Introduction and Problem Statement
178(1)
9.2 FCM for Relational Data
179(2)
9.3 Decomposition of Fuzzy Relational Patterns
181(7)
9.3.1 Gradient-Based Solution to the Decomposition Problem
182(2)
9.3.2 Neural Network Model of the Decomposition Problem
184(4)
9.4 Comparative Analysis
188(1)
9.5 Conclusions
189(1)
References
189(2)
10 Fuzzy Clustering of Heterogeneous Patterns 191(18)
10.1 Introduction
191(1)
10.2 Heterogeneous Data
192(2)
10.3 Parametric Models of Granular Data
194(1)
10.4 Parametric Mode of Heterogeneous Fuzzy Clustering
195(3)
10.5 Nonparametric Heterogeneous Clustering
198(9)
10.5.1 A Frame of Reference
198(2)
10.5.2 Representation of Granular Data Through the Possibility-Necessity Transformation
200(5)
10.5.3 Dereferencing
205(2)
10.6 Conclusions
207(1)
References
208(1)
11 Hyperbox Models of Granular Data: The Tchebyschev FCM 209(17)
11.1 Introduction
209(1)
11.2 Problem Formulation
210(1)
11.3 The Clustering Algorithm Detailed Considerations
211(7)
11.4 Development of Granular Prototypes
218(2)
11.5 Geometry of Information Granules
220(3)
11.6 Granular Data Description: A General Model
223(1)
11.7 Conclusions
223(1)
References
224(2)
12 Genetic Tolerance Fuzzy Neural Networks 226(20)
12.1 Introduction
226(1)
12.2 Operations of Thresholding and Tolerance: Fuzzy Logic-Based Generalizations
227(4)
12.3 Topology of the Logic Network
231(4)
12.4 Genetic Optimization
235(1)
12.5 Illustrative Numeric Studies
236(8)
12.6 Conclusions
244(1)
References
245(1)
13 Granular Prototyping 246(24)
13.1 Introduction
246(1)
13.2 Problem Formulation
247(4)
13.2.1 Expressing Similarity Between Two Fuzzy Sets
247(1)
13.2.2 Performance Index (Objective Function)
248(3)
13.3 Prototype Optimization
251(12)
13.4 Development of Granular Prototypes
263(5)
13.4.1 Optimization of the Similarity Levels
263(1)
13.4.2 An Inverse Similarity Problem
264(4)
13.5 Conclusions
268(1)
References
268(2)
14 Granular Mappings 270(13)
14.1 Introduction and Problem Statement
270(1)
14.2 Possibility and Necessity Measures as the Computational Vehicles of Granular Representation
271(1)
14.3 Building the Granular Mapping
272(3)
14.4 Designing Multivariable Granular Mappings Through Fuzzy Clustering
275(3)
14.5 Quantification of Granular Mappings
278(1)
14.6 Experimental Studies
278(2)
14.7 Conclusions
280(2)
References
282(1)
15 Linguistic Modeling 283(14)
15.1 Introduction
283(2)
15.2 Cluster-Based Representation of Input-Output Mapping
285(2)
15.3 Conditional Clustering in the Development of a Blueprint of Granular Models
287(3)
15.4 The Granular Neuron as a Generic Processing Element in Granular Networks
290(3)
15.5 The Architecture of Linguistic Models Based on Conditional Fuzzy Clustering
293(1)
15.6 Refinements of Linguistic Models
294(1)
15.7 Conclusions
295(1)
References
296(1)
Bibliography 297(18)
Index 315


WITOLD PEDRYCZ, PHD, is a Professor and Canada Research Chair at the University of Alberta, Canada. He is also with the Systems Research Institute of The Polish Academy of Sciences, Warsaw, Poland. Dr. Pedrycz is a Fellow of the IEEE, has authored nine research monographs, edited six volumes, and has written numerous papers in computational intelligence, granular computing, pattern recognition, quantitative software engineering, and data mining.