An improved fuzzy C-means clustering algorithm
Lingzi Duan, Fusheng Yu, Zhan Li · 2016
Sensitive to the initial number and centers of clusters is one shortcoming of fuzzy c-means clustering method. Aiming to reduce the sensitivity, a partial supervision-based fuzzy c-means clustering method is proposed in this paper. In this method, the data is first clustered with standard fuzzy c-means algorithm. If the clustering result doesn't accord with the structure of data, there must be one or more clusters that have been wrongly separated resulting in some clusters close to each other. The close clusters can be found by investigating the partition matrix. Those close clusters should be divided or merged. In both situations, approaches are then proposed in this new method to update the appropriate cluster number and cluster centers. With the updated cluster centers as labeled patterns, partially supervised fuzzy clustering is carried to give the appropriate clusters. Experiments on four synthetic datasets and a real dataset show that the proposed clustering method has good performance by comparing to the standard fuzzy c-means clustering method.