Normal view MARC view ISBD view

Algorithms for Fuzzy Clustering [electronic resource] :Methods in c-Means Clustering with Applications / by Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda.

by Miyamoto, Sadaaki [author.]; Ichihashi, Hidetomo [author.]; Honda, Katsuhiro [author.]; SpringerLink (Online service).

Material type: materialTypeLabel

BookSeries: Studies in Fuzziness and Soft Computing: 229Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2008.Description: online resource.ISBN: 9783540787372.Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)DDC classification: 519 Online resources: Click here to access online

Contents:

BasicMethods for c-Means Clustering -- Variations and Generalizations - I -- Variations and Generalizations - II -- Miscellanea -- Application to Classifier Design -- Fuzzy Clustering and Probabilistic PCA Model -- Local Multivariate Analysis Based on Fuzzy Clustering -- Extended Algorithms for Local Multivariate Analysis.

In: Springer eBooksSummary: The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means, and hence fuzzy c-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c-means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy c-means become complete by adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two.

Tags from this library: No tags from this library for this title. Add tag(s)

average rating: 0.0 (0 votes)

Item type	Current location	Call number	Status	Date due	Barcode
		TA640-643 (Browse shelf)	Available
Long Loan	MAIN LIBRARY	TA329-348 (Browse shelf)	Available

Browsing MAIN LIBRARY Shelves Close shelf browser

Previous								Next
Previous	GE1-350 The Buzz about Bees	TA329-348 Communications and Discoveries from Multidisciplinary Data	TA329-348 Modelling and Control of Dynamical Systems: Numerical Implementation in a Behavioral Framework	TA329-348 Algorithms for Fuzzy Clustering	QA76.7-76.73 Programming Languages and Systems	QA76.6-76.66 Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics	QA75.5-76.95 Applications of Evolutionary Computing	Next

The main subject of this book is the fuzzy c-means proposed by Dunn and Bezdek and their variations including recent studies. A main reason why we concentrate on fuzzy c-means is that most methodology and application studies in fuzzy clustering use fuzzy c-means, and hence fuzzy c-means should be considered to be a major technique of clustering in general, regardless whether one is interested in fuzzy methods or not. Unlike most studies in fuzzy c-means, what we emphasize in this book is a family of algorithms using entropy or entropy-regularized methods which are less known, but we consider the entropy-based method to be another useful method of fuzzy c-means. Throughout this book one of our intentions is to uncover theoretical and methodological differences between the Dunn and Bezdek traditional method and the entropy-based method. We do note claim that the entropy-based method is better than the traditional method, but we believe that the methods of fuzzy c-means become complete by adding the entropy-based method to the method by Dunn and Bezdek, since we can observe natures of the both methods more deeply by contrasting these two.

Koha online

Algorithms for Fuzzy Clustering [electronic resource] :Methods in c-Means Clustering with Applications / by Sadaaki Miyamoto, Hidetomo Ichihashi, Katsuhiro Honda.

by Miyamoto, Sadaaki [author.]; Ichihashi, Hidetomo [author.]; Honda, Katsuhiro [author.]; SpringerLink (Online service).

Browsing MAIN LIBRARY Shelves Close shelf browser