Multilayered fuzzy clustering method based on distance and density

In this paper, a multilayered fuzzy clustering method based on distance and density (MFCDD) is proposed. The first layer´s algorithm deals with the original data points, the upper with the cluster centers of the contiguous lower layer. In each layer it identifies the cluster number automatically. It calculates the density and density set of each data point based on distance matrix; then chooses one data point randomly and judges whether every element in the selected data point´s density set is in the same cluster with itself, this process is repeated till all data points have been selected. In order to find the optimum value of the parameters, we adopt an objective function using entropy on the upmost layer. Clustering analysis of MFCDD has been performed and the experimental results show that a high recognition rate can be achieved.