doi: 10.7763/IJFCC.2015.V4.384
A Comparative Study of Multi-SOM Algorithms for Determining the Optimal Number of Clusters
I. Khanchouch
1
,
M. Charrad
2
,
M. Limam
3,4
- 1 LARODEC Laboratory and High Institute of Management, ISG Tunis, University of Tunis, Tunisia.
- 2 University of Gabes, Tunisia.
- 3 University of Tunis
- 4 Dhofar University, Oman.
Abstract
The interpretation of the quality of clusters and the determination of the optimal number of clusters is still a crucial problem in clustering.<br />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.
Keywords
- Clustering
- SOM
- multi-SOM
- DVI
- DB index
- Dunn index
- Silhouette index
How to Cite
I. Khanchouch, M. Charrad, and M. Limam, "A Comparative Study of Multi-SOM Algorithms for Determining the Optimal Number of Clusters," International Journal of Future Computer and Communication, vol. 4, no. 3, pp. 198-202, 2015. https://doi.org/ 10.7763/IJFCC.2015.V4.384
Copyright & License
Copyright © 2015 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).