Identifying moving variance to make automatic clustering for normal data set

This paper proposed new approach to make cluster construction automatically for normal data set. The proposed method for automatic cluster construction is based on identifying moving variance of cluster for each stage of cluster construction, then analyzing the pattern to find the global optimum. After that, this paper proposed a new formulation to stop the construction of the cluster where it is in the global optimum, as well as to avoid the local optima. Experiment results will perform the effectiveness of the proposed method in this paper.

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