One of the main challenges in the field of c-means clustering
models is creating an algorithm that is both accurate and
robust. In the absence of outlier data, the conventional
probabilistic fuzzy c-means (FCM) algorithm, or the latest
possibilistic-fuzzy mixture model (PFCM), provide highly
accurate partitions. However, during the 30-year history of
FCM, the researcher community of the field failed to produce
an algorithm that is accurate and insensitive to outliers at
the same time. This paper introduces a novel mixture
clustering model built upon probabilistic and possibilistic
fuzzy partitions, where the two components are connected to
each other in a qualitatively different way than they were in
earlier mixtures. The fuzzy-possibilistic product partition c-
means (FP3CM) clustering algorithm seems to fulfil the initial
requirements, namely it successfully suppresses the effect of
outliers situated at any finite distance and provides
partitions of high quality.
