Fuzzy K-medoids clustering models for fuzzy multivariate time trajectories

Abstract

Following the fuzzy approach, the clustering problem concerning a set of fuzzy multivariate time trajectories is addressed. The obtained clusters are characterized by observed typical LR fuzzy time trajectories, medoids, belonging to the data set at hand. Two different clustering models are proposed according to the cross-sectional or longitudinal aspects of the time trajectories. An application to air pollution data is carried out.