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Possibilistic fuzzy c-means (PFCM) algorithm is a reliable algorithm has beenproposed to deal the weakness of two popular algorithms for clustering, fuzzyc-means (FCM) and possibilistic c-means (PCM). PFCM algorithm deals with theweaknesses of FCM in handling noise sensitivity and the weaknesses of PCM inthe case of coincidence clusters. However, the PFCM algorithm can be onlyapplied to cluster complete data sets. Therefore, in this study, we propose amodification of the PFCM algorithm that can be applied to incomplete data setsclustering. We modified the PFCM algorithm to OCSPFCM and NPSPFCM algorithmsand measured performance on three things: 1) accuracy percentage, 2) a numberof iterations to termination, and 3) centroid errors. Based on the results thatboth algorithms have the potential for clustering incomplete data sets.However, the performance of the NPSPFCM algorithm is better than the OCSPFCMalgorithm for clustering incomplete data sets.

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