Automatic fuzzy clustering based on mistake analysis

This paper presents a robust fuzzy clustering algorithm which can perform clustering without pre-assigning the number of clusters and is not sensitive to the initialization of cluster centers. This is achieved by iteratively splitting and merging operations under the guidance of mistake measurements. In every step of the iteration, we first split the cluster containing data points belonging to different classes, and then merge the wrongly divided cluster pair. A validity index is proposed based on the two mistake measurements to determine the termination of the clustering process. Experimental results confirm the effectiveness and robustness of the proposed clustering algorithm.