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Hybrid Self Organizing Map using Monte Carlo Computing
Sung-Hee Jun(전성해),Minjae Park(박민재),Kyung-Whan Oh(오경환) 한국지능시스템학회 2006 한국지능시스템학회 학술발표 논문집 Vol.16 No.1
Self Organizing Map(SOM) is a powerful neural network model for unsupervised learning. In many clustering works with exploratory data analysis, it has been popularly used. But it has a weakness which is the poorly theoretical base. A lot more researches for settling the problem have been published. Also, our paper proposes a method to overcome the drawback of SOM. As compared with the presented researches, our method has a different approach to solve the problem. So, a hybrid SOM is proposed in this paper. Using Monte Carlo computing, a hybrid SOM improves the performance of clustering. We verify the improved performance of a hybrid SOM according to the experimental results using UCI machine learning repository. In addition to, the number of clusters is determined by our hybrid SOM.