Modified Possibilistic Fuzzy C-Means Algorithm for Clustering Incomplete Data Sets

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...

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Bibliographic Details
Published in:arXiv.org 2020-07
Main Authors: Rustam, Koredianto Usman, Kamaruddin, Mudyawati, Chamidah, Dina, Nopendri, Saleh, Khaerudin, Eliskar, Yulinda, Marzuki, Ismail
Format: Article
Language:English
Subjects:
Algorithms
Centroids
Clustering
Datasets
Fuzzy sets
Noise sensitivity
Online Access:Get full text
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Summary: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.
ISSN:2331-8422