Fuzzy and possibilistic approach to clustering of imprecise data

This work focuses on clustering data affected by imprecision. The imprecision is managed by fuzzy sets, in particular, LR fuzzy data. The clustering process is based on the fuzzy and possibilistic approach. In both the approaches the observations are assigned to the clusters by means of membership degrees. In fuzzy clustering the membership degrees express the degrees of sharing of the observations to the clusters whereas, in possibilistic clustering, these give the degrees of typicality. These two sources of information are not exclusive because the former helps to discover the best fuzzy partition of the observations and the latter gives how well the observations are described by the cluster prototypes. We propose an hybridization of the fuzzy and the possibilistic algorithms exploiting the benefits of both the approaches.