Fuzzy C-Mean Clustering Algorithm Combining Inter-Cluster Distance

With the rapid and vigorous development of fuzzy clustering theory and methods, more fuzzy clustering algorithms have been proposed to establish the uncertainty description of the samples. However, when clustering is performed, existing fuzzy clustering algorithms mostly iterate feature weights or deal with noise.The objective function is mostly based on minimizing the Euclidean distance within the clusters. However, increasing the Euclidean distance between cluster centroids may also lead to an improvement in clustering performance.In this paper, a new fuzzy c-mean clustering algorithm (JCFCM) combining inter-cluster distances is proposed. Not only is an affiliation assigned within the original cluster, but it is also reflected in the form of affiliation between clusters.In this paper, clustering is performed by increasing the process of iterative selection of cluster centers between clusters. With this formalization an objective function is designed and the iterative formulas for the parameters in the function are obtained by solving the objective function optimally. Finally, experiments are conducted on five real data sets and compared with other fuzzy clustering algorithms. Overall, the JCFCM algorithm has better clustering results than the fuzzy C-mean algorithm and has some advantages over the existing improved fuzzy C-mean algorithm for different data sets.