Multidimensional Scaling(MDS), Self-Organizing Map(SOM) and K-means method are common tools for clustering analysis in market segmentation. This study investigate validity analysis of alternative clustering results obtained using the algorithm named MSK(MDS+SOM+K-means) composite model. Thus, the MSK uses the combined MDS and SOM model to determine an initial solution, and then applies K-means algorithm to find the final solution for the rice market segmentation. In order to verify the MSK composite model, the subjects selected for the analysis were 284 housewives living in Seoul. The reported results show that the MSK model is significantly better than the MDS, SOM, K-means, and MDS+SOM model with respect to mean within cluster variations(MWCV). As a result, the MSK model showed the optimal segments number and the rice market in Korea was divided into 9 segments. Each segment was identified by distinctive characteristics such as consumer behavior, demographic characteristics and purchasing attitudes. Therefore, there are consumer groups with various shopping orientations and purchasing behaviors in the rice market.