Knowledge-Based Clustering : From Data to Information Granules
Witold Pedrycz
- 出版商: Wiley
- 出版日期: 2005-01-01
- 售價: $5,340
- 貴賓價: 9.5 折 $5,073
- 語言: 英文
- 頁數: 336
- 裝訂: Hardcover
- ISBN: 0471469661
- ISBN-13: 9780471469667
-
相關分類:
大數據 Big-data、Data Science
海外代購書籍(需單獨結帳)
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商品描述
Descriptions:
* 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
Table of Contents:
Preface.
1. Clustering and Fuzzy Clustering.
1. Introductory Comments.
2. The 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.
商品描述(中文翻譯)
描述：
* 全面涵蓋新興和當前科技，處理異質信息來源，包括數據、設計提示、來自外部數據集的強化信號以及相關主題
* 包含所有必要的先決條件，並在必要時提供更高級主題的附加解釋，以使抽象概念更具體
* 包含插圖材料和著名實驗，提供實踐經驗
目錄：
前言
1. 聚類和模糊聚類
1. 簡介
2. 基本概念和符號
2.1 數據類型
2.2 距離和相似度
3. 聚類算法的主要類別
3.1 階層聚類
3.2 基於目標函數的聚類
4. 聚類和分類
5. 模糊聚類
6. 聚類的有效性
7. 基於目標函數的模糊聚類的擴展
7.1 模糊聚類的擴展幾何：模糊C-變體
7.2 可能性聚類
7.3 噪聲聚類
8. 自組織映射和基於目標函數的模糊聚類
9. 結論
參考文獻
2. 使用粒狀信息進行計算：模糊集和模糊關係
1. 粒狀計算的範例：信息粒和其處理
2. 模糊集作為以人為中心的信息粒
3. 模糊集的操作
4. 模糊關係
5. 兩個模糊集的比較
6. 模糊集的泛化
7. 陰影集
8. 粗糙集
9. 粒狀計算和分佈式處理
10. 結論
參考文獻
3. 邏輯導向的神經計算
1. 簡介
2. 模糊神經元的主要類別
2.1 聚合神經元
2.2 參考神經元
3. 邏輯網絡的結構
4. 網絡的解釋方面
5. 邏輯處理的粒狀界面
6. 結論
