Possibilistic approach to clustering of interval data
Bruno Almeida Pimentel, Renata M.C.R. de Souza · 2012
Clustering analysis is an important tool used in several application domains like pattern recognition, computer vision and computational biology to summarize data. The fuzzy c-means method (FCM) is the most popular fuzzy clustering algorithm, however this method is sensitive to noisy data. The possibilistic c-means (PCM) was created as an alternative to solve this problem. The propose in this work is extend the classical PCM to symbolic interval-valued data. Experiments with artificial and real symbolic interval-type data sets are performed and the results show the superiority of PCM in relation to FCM methods to interval-valued data.