A new fuzzy clustering method based on distance and density

摘要

Fuzzy clustering in capable of finding vague boundaries that crisp fails to obtain. But time complexity of fuzzy clustering is usually high, and need to specify complicated parameter hinders its use. In thin paper, a new fuzzy clustering method based on distance and density (FCDD) is proposed. It 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 till all data points has been chosen. Unlike previous method of its kind, it does not require finding cluster center and the density values are calculated only once. This saves a lot of time. Also 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 method above on several data set has been performed and the experimental results show that a high recognition rate can be achieved.