Multi-Clustering Centers Approach to Enhancing the Performance of SOM Clustering Ability

This paper modified the mechanism of weight adjusting of the Self-Organizing Mapping network (SOM) for solving the problems of topology preserving and clarifying boundary of clustering graph for the clustering analysis. The modified SOM is named the Multiple Clustering Centers SOM (MCC-SOM). The MCC-SOM changed the competitive learning mechanism of “winner takes all” to allow the more one clustering centers that can cause the graph of neighboring clusters with blurring boundary to focus on each of the cluster centers, and highlight the boundary of each cluster. The mechanism also can automatically set the units of topology without appropriate setting, and promote the topology preserving of graph by the consistency between the standard of weight adjustment and the case. Through the case studies, it is evident that the MCC-SOM can modify the performance of the SOM model. Thus, by using the MCC-SOM, the analysts can use the output topology to produce more precise result of classification of cases, and enhance the correct percent of the afterward predicting or classifying models.