  Fuzzy clustering methods allow the objects to belong to several clusters
simultaneously, with different degrees of membership. However, a factor that
influences the performance of fuzzy algorithms is the value of fuzzifier
parameter. In this paper, we propose a fuzzy clustering procedure for data
(time) series that does not depend on the definition of a fuzzifier parameter.
It comes from two approaches, theoretically motivated for unsupervised and
supervised classification cases, respectively. The first is the Probabilistic
Distance (PD) clustering procedure. The second is the well known Boosting
philosophy. Our idea is to adopt a boosting prospective for unsupervised
learning problems, in particular we face with non hierarchical clustering
problems. The aim is to assign each instance (i.e. a series) of a data set to a
cluster. We assume the representative instance of a given cluster (i.e. the
cluster center) as a target instance, a loss function as a synthetic index of
the global performance and the probability of each instance to belong to a
given cluster as the individual contribution of a given instance to the overall
solution. The global performance of the proposed method is investigated by
various experiments.
