RECM: Relational Evidential c-means algorithm

Marie-Hélène Masson (1) , Thierry Denoeux (1)

1 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]

A new clustering algorithm for proximity data, called RECM (relational evidential c-means) is presented. This algorithm generates a credal partition, a new clustering structure based on the theory of belief functions, which extends the existing concepts of hard, fuzzy and possibilistic partitions. Two algorithms, EVCLUS (Evidential Clustering) and ECM (evidential c-means) were previously available to derive credal partitions from data. EVCLUS was designed to handle proximity data, whereas ECM is a direct extension of fuzzy clustering algorithms for vectorial data. In this article, the relational version of ECM is introduced. It is compared to EVCLUS using various datasets. It is shown that RECM provides similar results to those given by EVCLUS. However, the optimization procedure of RECM, based on an alternate minimization scheme, is computationally much more efficient than the gradient-based procedure used in EVCLUS.

Domaines: Vision par ordinateur et reconnaissance de formes [cs.CV]

Marie-Hélène Masson, Thierry Denoeux. RECM: Relational Evidential c-means algorithm. Pattern Recognition Letters, 2009, 30, pp.1015-1026. ⟨10.1016/j.patrec.2009.04.008⟩. ⟨hal-00400273⟩