Partitioning Methods On Dissimilarity Matrices Set

Francisco F. A.T. de Carvalho (1) , Yves Lechevallier (2)

1 CIn - Centro de Informatica UFPE [Recife]
2 AxIS - Usage-centered design, analysis and improvement of information systems

We introduce 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.

Mots clés: Partitioning Clustering Algorithms; Relational Data; Collaborative Clustering; Multiple Dissimilarity Matrices

Domaines: Machine Learning [stat.ML]

Francisco F. A.T. de Carvalho, Yves Lechevallier. Partitioning Methods On Dissimilarity Matrices Set. European Conference on Data Analysis, GfKl and SFC, Jul 2013, Luxembourg, Luxembourg. ⟨hal-00916906⟩