Knowledge-based clustering : from data to information granules

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Knowledge-based clustering : from data to information granules / Witold Pedrycz

資料種別:: 図書
出版情報:: Hoboken, N.J. : Wiley-Interscience, c2005
形態:: xvii, 316 p. ; 25 cm
著者名:: Pedrycz, Witold, 1953- <DA03634844>
ISBN:: 9780471469667 [0471469661] (: cloth)
書誌ID:: BA71424157

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注記:

Includes bibliographical references (p. 297-313) and index

主題:

Fuzzy systems; Granular computing; Soft computing

言語:

英語

目次情報:

Foreword

Preface

Clustering and Fuzzy Clustering / 1：

Introduction

Basic Notions and Notation / 2：

Types of Data / 2.1：

Distance and Similarity / 2.2：

Main Categories of Clustering Algorithms / 3：

Hierarchical Clustering / 3.1：

Objective Function - Based Clustering / 3.2：

Clustering and Classification / 4：

Fuzzy Clustering / 5：

Cluster Validity / 6：

Extensions of Objective Function-Based Fuzzy Clustering / 7：

Augmented Geometry of Fuzzy Clusters: Fuzzy C-Varieties / 7.1：

Possibilistic Clustering / 7.2：

Noise Clustering / 7.3：

Self Organizing Maps and Fuzzy Objective Function Based Clustering / 8：

Conclusions / 9：

References

Computing with Granular Information: Fuzzy Sets and Fuzzy Relations

A Paradigm of Granular Computing: Information Granules and their Processing

Fuzzy Sets as Human-Centric Information Granules

Operations on Fuzzy Sets

Fuzzy Relations

Comparison of Two Fuzzy Sets

Generalizations of Fuzzy Sets

Shadowed Sets

Rough Sets

Granular Computing and Distributed Processing

Logic-Oriented Neurocomputing / 10：

Main Categories of Fuzzy Neurons

Aggregative Neurons

Referential (reference) Neurons

Architectures of Logic Networks

Interpretation Aspects of the Networks

The Granular Interfaces of Logic Processing

Conditional Fuzzy Clustering

Problem Statement: Context Fuzzy Sets and Objective Function

The Optimization Problem

Computational Considerations of Conditional Clustering

Generalizations of the Algorithm Through the Aggregation Operator

Fuzzy Clustering with Spatial Constraints

Clustering with Partial Supervision

Problem Formulation

The Design of the Clusters

Experimental Examples

Cluster-Based Tracking Problem

Principles of Knowledge-Based Guidance in Fuzzy Clustering

Examples of Knowledge-Oriented Hints and their General Taxonomy

The Optimization Environment of Knowledge-Enhanced Clustering

Quantification of Knowledge-Based Guidance Hints and Their Optimization

The Organization of the Interaction Process

Proximity - Based Clustering (P-FCM

Web Exploration and P-FCM

Linguistic Augmentation of Knowledge-Based Hints

Concluding Comments

Collaborative Clustering

Introduction and Rationale

Horizontal and Vertical Clustering

Horizontal Collaborative Clustering

Optimization Details

The Flow of Computing of Collaborative Clustering

Quantification of the Collaborative Phenomenon of the Clustering / 3.3：

Experimental Studies

Further Enhancements of Horizontal Clustering

The Algorithm of Vertical Clustering

A Grid Model of Horizontal and Vertical Clustering

Consensus Clustering

Directional Clustering

The Objective Function

The Logic Transformation Between Information Granules

The Algorithm

The Overall Development Framework of Directional Clustering

Numerical Studies

Fuzzy Relational Clustering

Introduction and Problem Statement

FCM for Relational Data

Decomposition of Fuzzy Relational Patterns

Gradient-Based Solution to the Decomposition Problem

Neural Network Model of the Decomposition Problem

Comparative Analysis

Fuzzy Clustering of Heterogeneous Patterns

Heterogeneous Data

Parametric Models of Granular Data

Parametric Mode of Heterogeneous Fuzzy Clustering

Nonparametric Heterogeneous Clustering

A Frame of Reference / 5.1：

Representation of Granular Data Through the Possibility-Necessity Transformation / 5.2：

Dereferencing / 5.3：

Hyperbox Models of Granular Data: The Tchebyschev FCM / 11：

The Clustering Algorithm-Detailed Considerations

The Development of Granular Prototypes

The Geometry of Information Granules

Granular Data Description: A General Model

Genetic Tolerance Fuzzy Neural Networks / 12：

Operations of Thresholdings and Tolerance: Fuzzy Logic-Based Generalizations

The Topology of the Logic Network

Genetic Optimization

Illustrative Numeric Studies

Granular Prototyping / 13：

Expressing Similarity Between Two Fuzzy Sets

Performance Index / objective function

Prototype Optimization

Optimization of the Similarity Levels / 4.1：

An Inverse Similarity Problem / 4.2：

Granular Mappings / 14：

Possibility and Necessity measure as the Computational Vehicle of Granular Representation

Building the Granular Mapping

The Design of Multivariable Granular Mappings Through Fuzzy Clustering

Quantification of Granular Mappings

Linguistic Modeling / 15：

The Cluster-Based Representation of the Input - Output Mapping

Conditional Clustering in the development of a blueprint of granular models

Granular neuron as a Generic Processing Element in Granular Networks

The Architecture of Linguistic Models Based on Conditional Fuzzy Clustering

Refinements of Linguistic Models

Bibliography

Index

Foreword

Preface

Clustering and Fuzzy Clustering / 1：

Introduction

Basic Notions and Notation / 2：

Types of Data / 2.1：

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