U*F Clustering: A New Performant “Cluster-Mining” Method Based On Segmentation of Self-Organizing Maps

Abstract – In this paper, we propose a new clustering method consisting in automated “flood- fill segmentation ” of the U*-matrix of a Self-Organizing Map after training. Using several artificial datasets as a benchmark, we find that the clustering results of our U*F method are good over a wide range of critical dataset types. Furthermore, comparison to standard clustering algorithms (K-means, single-linkage and Ward) directly applied on the same datasets show that each of the latter performs very bad on at least one kind of dataset, contrary to our U*F clustering method: while not always the best, U*F clustering has the great advantage of exhibiting consistently good results. Another advantage of U*F is that the computation cost of the SOM segmentation phase is negligible, contrary to other SOM-based clustering approaches which apply O(n2logn) standard clustering algorithms to the SOM prototypes. Finally, it should be emphasized that U*F clustering does not require a priori knowledge on the number of clusters, making it a real “cluster-mining ” algorithm