On new sequential hard c-means and its kernelization

This paper presents a new sequential clustering algorithm based on sequential hard c-means clustering. The word sequential cluster extraction means that the algorithm extract one cluster at a time. The sequential hard c-means is one of the typical and conventional sequential clustering methods. The proposed new sequential clustering algorithm is based on Dave´s noise clustering approach. A characteristic parameter which is called noise parameter is applied in Dave´s approach. We construct a new sequential hard c-means algorithm by introducing another new parameter which controls a number of extracting objects and considering the noise parameter as a variables in optimization problem. First, the optimization problem of new sequential hard c-means clustering is introduced. Next, the sequential clustering algorithm and its kernelization are constructed based on above optimization problem. Moreover, the effectiveness of proposed method is shown through numerical experiments.