A new fuzzy clustering method based on distance and density

Fuzzy clustering is capable of finding vague boundaries, but its time complexity is usually high, and the need to specify complicated parameters hinders its use. Here, a new fuzzy clustering method based on distance and density (FCDD) is proposed, which automatically identifies the cluster number. It calculates the density and density set of each data point and selects any data point at the beginning of the FCDD algorithm. Next it judges whether every element in the chosen data point´s density set is in the same cluster with itself. This process is repeated until all data points has been chosen. The method does not require finding the cluster center and density values are calculated only once. It requires two parameters that are easy to specify and is able to find the natural clusters in the data. In order to find the optimum values of the parameters with respect to the specified number of the cluster, we construct a target function using entropy. Cluster analysis of the two methods has been performed on several data sets and the experimental results show that a high recognition rate can be achieved.