Proceedings of the Korean Institute of Intelligent Systems Conference (한국지능시스템학회:학술대회논문집)
- 2006.05a
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- Pages.381-384
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- 2006
Hybrid Self Organizing Map using Monte Carlo Computing
- Jun Sung-Hae (Department of Statistics, Cheongju University) ;
- Park Min-Jae (Pentech) ;
- Oh Kyung-Whan (Department of Computer Science, Sogang University)
- Published : 2006.05.01
Abstract
Self Organizing Map(SOM) is a powerful neural network model for unsupervised loaming. 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 loaming repository. In addition to, the number of clusters is determined by our hybrid SOM.