Modified Possibilistic Fuzzy C-Means Algorithm for Clustering Incomplete Data Sets
Abstract
Possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm has been proposed to deal the weakness of two popular algorithms for clustering, fuzzy c-means (FCM) and possibilistic c-means (PCM). PFCM algorithm deals with the weaknesses of FCM in handling noise sensitivity and the weaknesses of PCM in the case of coincidence clusters. However, the PFCM algorithm can be only applied to cluster complete data sets. Therefore, in this study, we propose a modification of the PFCM algorithm that can be applied to incomplete data sets clustering. We modified the PFCM algorithm to OCSPFCM and NPSPFCM algorithms and measured performance on three things: 1) accuracy percentage, 2) a number of iterations to termination, and 3) centroid errors. Based on the results that both algorithms have the potential for clustering incomplete data sets. However, the performance of the NPSPFCM algorithm is better than the OCSPFCM algorithm for clustering incomplete data sets.
Cite
@article{arxiv.2007.04908,
title = {Modified Possibilistic Fuzzy C-Means Algorithm for Clustering Incomplete Data Sets},
author = {Rustam and Koredianto Usman and Mudyawati Kamaruddin and Dina Chamidah and Nopendri and Khaerudin Saleh and Yulinda Eliskar and Ismail Marzuki},
journal= {arXiv preprint arXiv:2007.04908},
year = {2020}
}
Comments
13 pages, 13 figures, submitted to Acta Polytechnica as scientific journal published by the Czech Technical University in Prague