A suspect point recheck method of fuzzy clustering for robot self-position estimation

For autonomous robots, the Fuzzy C-means algorithm (FCM) is used in the tasks like self-position estimation, path planning and environment navigation. This paper proposes a suspect point recheck method for fuzzy clustering algorithm. First, the proposed method works as the typical FCM to obtain an original clustering result. Then the method classifies all the data points into normal points and suspect points according to their memberships of each cluster. Finally, the method redistributes the suspect points according to the information of their nearby normal points. Three datasets from UCI Machine Learning Repository are used in the experiments. The experimental results verify that the proposed method has higher clustering capability.