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Estimation of Bounds of the Set of Potential Number of Fuzzy Clusters in a Sought Clustering Structure

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Dmitri A. Viattchenin, Aliaksandr Yaroma, Aliaksandr Damaratski. Published in Fuzzy Systems.

Estimation of Bounds of the Set of Potential Number of Fuzzy Clusters in a Sought Clustering Structure
Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Series: Volume 6 Number 2
Authors: Dmitri A. Viattchenin, Aliaksandr Yaroma, Aliaksandr Damaratski
10.5120/cae2016652089
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Dmitri A Viattchenin, Aliaksandr Yaroma and Aliaksandr Damaratski. Estimation of Bounds of the Set of Potential Number of Fuzzy Clusters in a Sought Clustering Structure. Communications on Applied Electronics 6(2):1-10, November 2016. BibTeX

@article{10.5120/cae2016652089,
	author = {Dmitri A. Viattchenin and Aliaksandr Yaroma and Aliaksandr Damaratski},
	title = {Estimation of Bounds of the Set of Potential Number of Fuzzy Clusters in a Sought Clustering Structure},
	journal = {Communications on Applied Electronics},
	issue_date = {November 2016},
	volume = {6},
	number = {2},
	month = {Nov},
	year = {2016},
	issn = {2394-4714},
	pages = {1-10},
	numpages = {10},
	url = {http://www.caeaccess.org/archives/volume6/number2/673-2016652089},
	doi = {10.5120/cae2016652089},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

In this paper, an approach to constructing the set of values of the most possible number of fuzzy clusters in a sought clustering structure is proposed. The proposed approach is based on heuristic possibilistic clustering and fuzzy numbers. For the purpose, fuzzy numbers are described and algorithms of the heuristic approach to possibilistic clustering are considered in brief. A procedure for constructing the set of values of the most possible number of fuzzy clusters is described for the object data set. An application of the proposed technique to the Anderson’s iris data set is provided and some concluding remarks are stated.

References

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Keywords

Triangular Fuzzy Number, Gaussian Fuzzy Number, Cluster Validity, Heuristic Possibilistic Clustering, Tolerance Threshold.