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Selecting Parameters of the Fuzzy Possibilistic Clustering Algorithm

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Mohamed Fadhel Saad, Adel M. Alimi. Published in Fuzzy Systems.

Selecting Parameters of the Fuzzy Possibilistic Clustering Algorithm
Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Series: Volume 5 Number 10
Authors: Mohamed Fadhel Saad, Adel M. Alimi
10.5120/cae2016652389
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Mohamed Fadhel Saad and Adel M Alimi. Selecting Parameters of the Fuzzy Possibilistic Clustering Algorithm. Communications on Applied Electronics 5(10):42-52, September 2016. BibTeX

@article{10.5120/cae2016652389,
	author = {Mohamed Fadhel Saad and Adel M. Alimi},
	title = {Selecting Parameters of the Fuzzy Possibilistic Clustering Algorithm},
	journal = {Communications on Applied Electronics},
	issue_date = {September 2016},
	volume = {5},
	number = {10},
	month = {Sep},
	year = {2016},
	issn = {2394-4714},
	pages = {42-52},
	numpages = {11},
	url = {http://www.caeaccess.org/archives/volume5/number10/661-2016652389},
	doi = {10.5120/cae2016652389},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}
}

Abstract

Clustering has been widely used in pattern recognition, image processing, and data analysis. It aims to organize a collection of data items into clusters, such that items within a cluster are more similar to each other than they are in other clusters. The Fuzzy Possibilistic C-Means (FPCM) is one of the most popular clustering methods based on minimization of a criterion function. So the implementation of this algorithm requires a priori selection of some parameters: the fuzzy and the typical exponent, initialization of cluster centers. But the definition of these parameters at the moment is fixed in advanced and the initialization of centers is random; so the algorithm can give results not consistent. The determination of an optimal value for these parameters and the cluster centers at the beginning are problematic and remains an open problem. New procedures for choice of the optimal values of parameters and for initialization of centers were developed. Numerical results using data sets are used to illustrate the simplicity and effectiveness of the proposed procedures.

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Keywords

Fuzzy c-means, Possibilistic c-means, Fuzzy possibilistic c-means, K-means++.