A novel adaptive fuzzy c-means algorithm for interval data type
Renata M.C.R. de Souza, Leonardo Vieira de Carvalho, Nicomedes L. Cavalcanti · 2012
A novel extension of the fuzzy c-means clustering algorithm for interval data type based on an adaptive Euclidean distance is presented. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive Euclidean distance that changes at each algorithm iteration. Experiments with real and synthetic data sets show the usefulness of this method.