Entropy-Based Fuzzy C-Ordered-Means Clustering Algorithm

Fuzzy C-Means is a well-known fuzzy clustering technique. Although FCM can cover the uncertainty problem by forming overlapping clusters, it involves issues such as sensitivity to noise and outliers, the fuzzification parameter m, and the initial guess of cluster centers. There are various improvements made to FCM to tackle these limitations. One such extension, FCOM, has proved to be an efficient method for handling noise and outliers. However, it still suffers from the last two ones. In the present paper, inspired by FCOM, a new model is designed to solve these issues. Whereas varying the degree of fuzziness m leads to different clusters, determining the appropriate value optimizes solutions. However, manually tuning this parameter can be time-consuming, especially when dealing with large data sets. To mitigate the dependence on this parameter, the proposed model utilizes the entropy theory to control the uncertainty associated with the input data. Extensive evaluations are conducted on benchmark datasets to analyze the impact of m on cluster formation and clustering results. The competitive results confirm the effectiveness of the proposed model for handling fuzziness degree and its capability to accelerate convergence to optimal solutions. Moreover, the results show that the proposed model discovers vague boundaries precisely.