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 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.
