Knowledge-based clustering : from data to information granules
著者
書誌事項
Knowledge-based clustering : from data to information granules
Wiley-Interscience, c2005
- : cloth
大学図書館所蔵 全9件
  青森
  岩手
  宮城
  秋田
  山形
  福島
  茨城
  栃木
  群馬
  埼玉
  千葉
  東京
  神奈川
  新潟
  富山
  石川
  福井
  山梨
  長野
  岐阜
  静岡
  愛知
  三重
  滋賀
  京都
  大阪
  兵庫
  奈良
  和歌山
  鳥取
  島根
  岡山
  広島
  山口
  徳島
  香川
  愛媛
  高知
  福岡
  佐賀
  長崎
  熊本
  大分
  宮崎
  鹿児島
  沖縄
  韓国
  中国
  タイ
  イギリス
  ドイツ
  スイス
  フランス
  ベルギー
  オランダ
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  ノルウェー
  アメリカ
注記
Includes bibliographical references (p. 297-313) and index
内容説明・目次
内容説明
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
目次
Foreword. Preface.
1. Clustering and Fuzzy Clustering.
1. Introduction.
2. Basic Notions and Notation.
2.1 Types of Data.
2.2 Distance and Similarity.
3. Main Categories of Clustering Algorithms.
3.1 Hierarchical Clustering.
3.2 Objective Function - Based Clustering.
4. Clustering and Classification.
5. Fuzzy Clustering.
6. Cluster Validity.
7. Extensions of Objective Function-Based Fuzzy Clustering.
7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy C-Varieties.
7.2 Possibilistic Clustering.
7.3 Noise Clustering.
8. Self Organizing Maps and Fuzzy Objective Function Based Clustering.
9. Conclusions.
References.
2. Computing with Granular Information: Fuzzy Sets and Fuzzy Relations.
1. A Paradigm of Granular Computing: Information Granules and their Processing.
2. Fuzzy Sets as Human-Centric Information Granules.
3. Operations on Fuzzy Sets.
4. Fuzzy Relations.
5. Comparison of Two Fuzzy Sets.
6. Generalizations of Fuzzy Sets.
7. Shadowed Sets.
8. Rough Sets.
9. Granular Computing and Distributed Processing.
10. Conclusions.
References.
3. Logic-Oriented Neurocomputing.
1. Introduction.
2. Main Categories of Fuzzy Neurons.
2.1 Aggregative Neurons.
2.2 Referential (reference) Neurons.
3. Architectures of Logic Networks.
4. Interpretation Aspects of the Networks.
5. The Granular Interfaces of Logic Processing.
6. Conclusions.
References.
4. Conditional Fuzzy Clustering.
1. Introduction.
2. Problem Statement: Context Fuzzy Sets and Objective Function.
3. The Optimization Problem.
4. Computational Considerations of Conditional Clustering.
5. Generalizations of the Algorithm Through the Aggregation Operator.
6. Fuzzy Clustering with Spatial Constraints.
7. Conclusions.
References.
5. Clustering with Partial Supervision.
1. Introduction.
2. Problem Formulation.
3. The Design of the Clusters.
4. Experimental Examples.
5. Cluster-Based Tracking Problem.
6. Conclusions.
References.
6. Principles of Knowledge-Based Guidance in Fuzzy Clustering.
1. Introduction.
2. Examples of Knowledge-Oriented Hints and their General Taxonomy.
3. The Optimization Environment of Knowledge-Enhanced Clustering.
4. Quantification of Knowledge-Based Guidance Hints and Their Optimization.
5. The Organization of the Interaction Process.
6. Proximity - Based Clustering (P-FCM).
7. Web Exploration and P-FCM.
8. Linguistic Augmentation of Knowledge-Based Hints.
9. Concluding Comments.
References.
7. Collaborative Clustering.
1. Introduction and Rationale.
2. Horizontal and Vertical Clustering.
3. Horizontal Collaborative Clustering.
3.1 Optimization Details.
3.2 The Flow of Computing of Collaborative Clustering.
3.3 Quantification of the Collaborative Phenomenon of the Clustering.
4. Experimental Studies.
5. Further Enhancements of Horizontal Clustering.
6. The Algorithm of Vertical Clustering.
7. A Grid Model of Horizontal and Vertical Clustering.
8. Consensus Clustering.
9. Conclusions.
References.
8. Directional Clustering.
1. Introduction.
2. Problem Formulation.
2.1 The Objective Function.
2.2 The Logic Transformation Between Information Granules.
3. The Algorithm.
4. The Overall Development Framework of Directional Clustering.
5. Numerical Studies.
6. Conclusions.
References.
9. Fuzzy Relational Clustering.
1. Introduction and Problem Statement.
2. FCM for Relational Data.
3. Decomposition of Fuzzy Relational Patterns.
3.1 Gradient-Based Solution to the Decomposition Problem.
3.2 Neural Network Model of the Decomposition Problem.
4. Comparative Analysis.
5. Conclusions.
References.
10. Fuzzy Clustering of Heterogeneous Patterns.
1. Introduction.
2. Heterogeneous Data.
3. Parametric Models of Granular Data.
4. Parametric Mode of Heterogeneous Fuzzy Clustering.
5. Nonparametric Heterogeneous Clustering.
5.1 A Frame of Reference.
5.2 Representation of Granular Data Through the Possibility-Necessity Transformation.
5.3 Dereferencing.
6. Conclusions.
References.
11. Hyperbox Models of Granular Data: The Tchebyschev FCM.
1. Introduction.
2. Problem Formulation.
3. The Clustering Algorithm-Detailed Considerations.
4. The Development of Granular Prototypes.
5. The Geometry of Information Granules.
6. Granular Data Description: A General Model.
7. Conclusions.
References.
12. Genetic Tolerance Fuzzy Neural Networks.
1. Introduction.
2. Operations of Thresholdings and Tolerance: Fuzzy Logic-Based Generalizations.
3. The Topology of the Logic Network.
4. Genetic Optimization.
5. Illustrative Numeric Studies.
6. Conclusions.
References.
13. Granular Prototyping.
1. Introduction.
2. Problem Formulation.
2.1 Expressing Similarity Between Two Fuzzy Sets.
2.2 Performance Index (objective function).
3. Prototype Optimization.
4. The Development of Granular Prototypes.
4.1 Optimization of the Similarity Levels.
4.2 An Inverse Similarity Problem.
5. Conclusions.
References.
14. Granular Mappings.
1. Introduction and Problem Statement.
2. Possibility and Necessity measure as the Computational Vehicle of Granular Representation.
3. Building the Granular Mapping.
4. The Design of Multivariable Granular Mappings Through Fuzzy Clustering.
5. Quantification of Granular Mappings.
6. Experimental Studies.
7. Conclusions.
References.
15. Linguistic Modeling.
1. Introduction.
2. The Cluster-Based Representation of the Input - Output Mapping.
3. Conditional Clustering in the development of a blueprint of granular models.
4. Granular neuron as a Generic Processing Element in Granular Networks.
5. The Architecture of Linguistic Models Based on Conditional Fuzzy Clustering.
6. Refinements of Linguistic Models.
7. Conclusions.
References.
Bibliography.
Index.
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