Delaunay simplices pruning based clustering

Octavio Razafindramanana (1) , Gilles Venturini (1)

1 LIFAT - Laboratoire d'Informatique Fondamentale et Appliquée de Tours

We introduce in this paper a new clustering method using the Delaunay triangulation of a set of points as an input. The proposed method is based on pruning away extra simplices of a triangulation accord- ing to a local heterogeneity measure which we introduce. This measure provides good clustering results as it yields to better inter-cluster simplices detection. Our introduced measure is evaluated on 2-D shape data set.

Domaines: Traitement des images [eess.IV]; Traitement du texte et du document; Vision par ordinateur et reconnaissance de formes [cs.CV]

Octavio Razafindramanana, Gilles Venturini. Delaunay simplices pruning based clustering. 21st European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Apr 2013, Bruges, Belgium. ⟨hal-01027480⟩