An efficient clustering of the SOM using rough set and genetic algorithm
Ehsan Mohebi, Asgarali Bouyer, Mohammad Bagher Karimi · 2009
The Kohonen self organizing map is an excellent tool in exploratory phase of data mining and pattern recognition. The SOM is a popular tool that maps high dimensional space into a small number of dimensions by placing similar elements close together, forming clusters. Recently researchers found that to capture the uncertainty involved in cluster analysis, it is not necessary to have crisp boundaries in some clustering operations. In this paper to overcome the uncertainty, an optimized two-level clustering algorithm based on SOM which employs the rough set theory and genetic algorithm is proposed. The two-level stage Genetic Rough SOM (first using SOM to produce the prototypes that are then clustered in the second stage based on rough set and genetic algorithm) is found to perform well and more accurate compared with the crisp clustering methods (i.e. Incremental SOM) and reduces the errors.