An Improved Competitive and Cooperative Learning Approach for Data Clustering

The recently proposed Competitive and Cooperative Learning algorithm(CCL) (Cheung 2004) has provided a promising way to perform the data clustering without know- ing the number of clusters. Nevertheless, its performance is somewhat sensitive to the initialization of seed points. Also, its cooperative mechanism is applicable to the homogenous clusters only. In this paper, we will therefore suggest using the FSCL algorithm to initialize the seed points such that each cluster of data will at least have a seed point. Fur- thermore, we update the cooperation radius of seed points in CCL, whereby the improved CCL (ICCL for short) can be applicable to the heterogeneous clusters as well. Exper- iments show the efficacy of the proposed algorithm.