Heuristic possibilistic clustering for detecting optimal number of elements in fuzzy clusters

Dmitri A. Viattchenin

doi:10.1515/fcds-2016-0003

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Heuristic possibilistic clustering for detecting optimal number of elements in fuzzy clusters

Foundations of Computing and Decision Sciences

Volume 41 (2016): Issue 1 (March 2016)

By: Dmitri A. Viattchenin

Open Access

|Mar 2016

Abstract

The paper deals with the problem of discovering fuzzy clusters with optimal number of elements in heuristic possibilistic clustering. The relational clustering procedure using a parameter that controls cluster sizes is considered and a technique for detecting the optimal number of elements in fuzzy clusters is proposed. The effectiveness of the proposed technique is illustrated through numerical examples. Experimental results are discussed and some preliminary conclusions are formulated.

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Articles in this issue

DOI: https://doi.org/10.1515/fcds-2016-0003 | Journal eISSN: 2300-3405 | Journal ISSN: 0867-6356

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Language: English

Page range: 45 - 76

Submitted on: Mar 24, 2015

Accepted on: Jan 20, 2016

Published on: Mar 31, 2016

Published by: Poznan University of Technology

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

possibilistic clustering,

Related subjects:

Artificial intelligence,

Software development

© 2016 Dmitri A. Viattchenin, published by Poznan University of Technology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Volume 41 (2016): Issue 1 (March 2016)