A new topological clustering algorithm for interval data

Guénaël Cabanes (1) , Younès Bennani (1) , Renaud Destenay (2) , André Hardy (2)

1 LIPN - Laboratoire d'Informatique de Paris-Nord
2 FUNDP - Facultés Universitaires Notre Dame de la Paix

Clustering is a very powerful tool for automatic detection of relevant subgroups in unlabeled data sets. In this paper we focus on interval data: i.e. where the objects are defined as hyper-rectangles. We propose here a new clustering algorithm for interval data, based on the learning of a Self Organizing Map. The major advantage of our approach is that the number of clusters to find is determined automatically; no a priori hypothesis for the number of clusters is required. Experimental results confirm the effectiveness of the proposed algorithm when applied to interval data.

Mots clés: Interval data; Clustering; Self-Organizing Map

Domaines: Analyse de données, Statistiques et Probabilités [physics.data-an]

Guénaël Cabanes, Younès Bennani, Renaud Destenay, André Hardy. A new topological clustering algorithm for interval data. Pattern Recognition, 2013, 46, pp.3030 - 3039. ⟨10.1016/j.patcog.2013.03.023⟩. ⟨hal-01461454⟩