Overview
- Offers an interesting and original perspective on possibilistic clustering and uncertain data processing
- Features a well-balanced material and a down-to-the earth exposition
- Represents an important contribution to the rapidly growing body of knowledge in contemporary data analysis
Part of the book series: Studies in Fuzziness and Soft Computing (STUDFUZZ)
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Table of contents (4 chapters)
Keywords
About this book
The present book outlines a new approach to possibilistic clustering in which the sought clustering structure of the set of objects is based directly on the formal definition of fuzzy cluster and the possibilistic memberships are determined directly from the values of the pairwise similarity of objects.   The proposed approach can be used for solving different classification problems. Here, some techniques that might be useful at this purpose are outlined, including a methodology for constructing a set of labeled objects for a semi-supervised clustering algorithm, a methodology for reducing analyzed attribute space dimensionality and a methods for asymmetric data processing. Moreover, a technique for constructing a subset of the most appropriate alternatives for a set of weak fuzzy preference relations, which are defined on a universe of alternatives, is described in detail, and a method for rapidly prototyping the Mamdani’s fuzzy inference systems is introduced. This book addresses engineers, scientists, professors, students and post-graduate students, who are interested in and work with fuzzy clustering and its applications
Authors and Affiliations
Bibliographic Information
Book Title: A Heuristic Approach to Possibilistic Clustering: Algorithms and Applications
Authors: Dmitri A. Viattchenin
Series Title: Studies in Fuzziness and Soft Computing
DOI: https://doi.org/10.1007/978-3-642-35536-3
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-35535-6Published: 01 May 2013
Softcover ISBN: 978-3-642-44301-5Published: 22 May 2015
eBook ISBN: 978-3-642-35536-3Published: 17 April 2013
Series ISSN: 1434-9922
Series E-ISSN: 1860-0808
Edition Number: 1
Number of Pages: XII, 227
Topics: Computational Intelligence, Data Mining and Knowledge Discovery, Artificial Intelligence