An Improved Multi-SOM Algorithm for Determining the Optimal Number of Clusters

The interpretation of the quality of clusters and the determination of the optimal number of clusters is still a crucial problem in cluster Analysis. In this paper, we focus in on multi-SOM clustering approach 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, and Silhouette index. The multi-SOM algorithm is compared to k-means and Birch methods. Results developed with R language show that it is more efficient than classical clustering methods.

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