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

Autor:	Rustam, Usman, Koredianto, Kamaruddin, Mudyawati, Chamidah, Dina, Nopendri, Saleh, Khaerudin, Eliskar, Yulinda, Marzuki, Ismail
Rok vydání:	2020
Předmět:	Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: 13 pages, 13 figures, submitted to Acta Polytechnica as scientific journal published by the Czech Technical University in Prague
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2007.04908 Zobrazit plný text záznamu View this record from Arxiv