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

Format:

Book

Author/Creator:

Pedrycz, Witold, 1953-

Language:

English

Subjects (All):

Soft computing.

Granular computing.

Fuzzy systems.

Physical Description:

1 online resource (336 p.)

Edition:

1st ed.

Place of Publication:

Hoboken, N.J. : Wiley, c2005.

Language Note:

English

Summary:

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 topicsCovers all necessary prerequisites, and if necessary,additional explanations of more advanced topics, to make abstract concepts more tangibleIncludes illustrative material andwell-known experimentsto offer hands-on experience

Contents:

KNOWLEDGE-BASED CLUSTERING; Contents; Foreword; Preface; 1 Clustering and Fuzzy Clustering; 1.1 Introduction; 1.2 Basic Notions and Notation; 1.2.1 Types of Data; 1.2.2 Distance and Similarity; 1.3 Main Categories of Clustering Algorithms; 1.3.1 Hierarchical Clustering; 1.3.2 Objective Function-Based Clustering; 1.4 Clustering and Classification; 1.5 Fuzzy Clustering; 1.6 Cluster Validity; 1.7 Extensions of Objective Function-Based Fuzzy Clustering; 1.7.1 Augmented Geometry of Fuzzy Clusters: Fuzzy C Varieties; 1.7.2 Possibilistic Clustering; 1.7.3 Noise Clustering

1.8 Self-Organizing Maps and Fuzzy Objective Function-Based Clustering1.9 Conclusions; References; 2 Computing with Granular Information: Fuzzy Sets and Fuzzy Relations; 2.1 A Paradigm of Granular Computing: Information Granules and Their Processing; 2.2 Fuzzy Sets as Human-Centric Information Granules; 2.3 Operations on Fuzzy Sets; 2.4 Fuzzy Relations; 2.5 Comparison of Two Fuzzy Sets; 2.6 Generalizations of Fuzzy Sets; 2.7 Shadowed Sets; 2.8 Rough Sets; 2.9 Granular Computing and Distributed Processing; 2.10 Conclusions; References; 3 Logic-Oriented Neurocomputing; 3.1 Introduction

3.2 Main Categories of Fuzzy Neurons3.2.1 Aggregative Neurons; 3.2.2 Referential (Reference) Neurons; 3.3 Architectures of Logic Networks; 3.4 Interpretation Aspects of the Networks; 3.5 Granular Interfaces of Logic Processing; 3.6 Conclusions; References; 4 Conditional Fuzzy Clustering; 4.1 Introduction; 4.2 Problem Statement: Context Fuzzy Sets and Objective Function; 4.3 The Optimization Problem; 4.4 Computational Considerations of Conditional Clustering; 4.5 Generalizations of the Algorithm Through the Aggregation Operator; 4.6 Fuzzy Clustering with Spatial Constraints; 4.7 Conclusions

References5 Clustering with Partial Supervision; 5.1 Introduction; 5.2 Problem Formulation; 5.3 Design of the Clusters; 5.4 Experimental Examples; 5.5 Cluster-Based Tracking Problem; 5.6 Conclusions; References; 6 Principles of Knowledge-Based Guidance in Fuzzy Clustering; 6.1 Introduction; 6.2 Examples of Knowledge-Oriented Hints and Their General Taxonomy; 6.3 The Optimization Environment of Knowledge-Enhanced Clustering; 6.4 Quantification of Knowledge-Based Guidance Hints and Their Optimization; 6.5 Organization of the Interaction Process; 6.6 Proximity-Based Clustering (P-FCM)

6.7 Web Exploration and P-FCM6.8 Linguistic Augmentation of Knowledge-Based Hints; 6.9 Conclusions; References; 7 Collaborative Clustering; 7.1 Introduction and Rationale; 7.2 Horizontal and Vertical Clustering; 7.3 Horizontal Collaborative Clustering; 7.3.1 Optimization Details; 7.3.2 The Flow of Computing of Collaborative Clustering; 7.3.3 Quantification of the Collaborative Phenomenon of Clustering; 7.4 Experimental Studies; 7.5 Further Enhancements of Horizontal Clustering; 7.6 The Algorithm of Vertical Clustering; 7.7 A Grid Model of Horizontal and Vertical Clustering

7.8 Consensus Clustering

Notes:

"A Wiley-Interscience publication."

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

ISBN:

9786610275472

9781280275470

1280275472

9780470243558

0470243554

9780471708599

0471708593

9780471708605

0471708607

OCLC:

609260264