This paper proposes a clustering algorithm based on the Self Organizing Map (SOM) method. To find the optimal number of clusters, our algorithm uses the Davies Bouldin index which has not been used previously in the multi-SOM. The proposed algorithm is compared to three clustering methods based on five databases. Results show that our algorithm is as performing as concurrent methods.
Abstract-The interpretation of the quality of clusters and the determination of the optimal number of clusters is still a crucial problem in clustering.We focus in this paper on multi-SOM clustering method which overcomes the problem of extracting the number of clusters from the SOM map through the use of a clustering validity index. We test the multi-SOM algorithm using real and artificial data sets with different evaluation criteria not used previously such as Davies Bouldin index, Dunn index and silhouette index. The multi-SOM algorithm is compared to k-means and Birch methods. Results show that it is more efficient than classical clustering methods.
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