In this paper we propose a noun clustering approach on the basis of CBC proposed by Pantel. CBC is a clustering approach that carefully extracts clusters by finding sub-clusters regarded as committees with the same meanings, and try to extract unknown clusters from the remaining elements. In preliminary experiments of Japanese noun clustering, however, we found that CBC does not work well at the measurement of basic similarity between words with context vectors and scoring method that decides to merge sub-clusters. To these problems in this paper we propose to apply Jensen-Shannon formula as a measurement and a new scoring method. In the experimental results of constructing sub-clusters of Japanese nouns from a new paper article we will show that our proposed approaches overcome the approaches in CBC at the clustering accuracy.