A New Clustering Algorithm Based on Regions of Influence with Self-Detection of the Best Number of Clusters Auteur(s): Muhlenbach Fabrice, Lallich S. Conference: The Ninth IEEE International Conference on Data Mining (Miami, Florida, US, 2009-12-06) Actes de conférence: Proceeding of the Ninth IEEE International Conference on Data Mining, vol. p.884-888 (2009) Ref HAL: hal-00446155_v1 Résumé: Clustering methods usually require to know the best number of clusters, or another parameter, e.g. a threshold, which is not ever easy to provide. This paper proposes a new graph-based clustering method called GBC'' which detects automatically the best number of clusters, without requiring any other parameter. In this method based on regions of influence, a graph is constructed and the edges of the graph having the higher values are cut according to a hierarchical divisive procedure. An index is calculated from the size average of the cut edges which self-detects the more appropriate number of clusters. The results of GBC for 3 quality indices (Dunn, Silhouette and Davies-Bouldin) are compared with those of K-Means, Ward's hierarchical clustering method and DBSCAN on 8 benchmarks. The experiments show the good performance of GBC in the case of well separated clusters, even if the data are unbalanced, non-convex or with presence of outliers, whatever the shape of the clusters. Commentaires: 6 pages