Clustering On Dissimilarity Matrices Set and Partitioning of Time Series
Yves Lechevallier · 2014
My talk introduces partitioning clustering models and algorithms that are able to partitioning objects taking into account simultaneously their relational descriptions given by multiple dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices have been generated using different sets of variables and dissimilarity functions.