Dynamic Metric Accelerated Method for Fuzzy Clustering

Abstract

Features in data samples usually need a unified dimension by a standardization process before clustering. However, there still exists a nonstandardized metric in which the distance between samples is greater than 1 after features are standardized. It is difficult to find the optimal search path if the data sample metrics are not standardized. To address this problem, we develop a dynamic-metric accelerated method for fuzzy clustering by introducing a metric matrix, whose diagonal elements consist of infinite norms of the metric matrix into the Fuzzy C-Means (FCM) clustering algorithm and its derived algorithms. More specifically, we focus on constructing a dynamic metric matrix that is used to unify the metric between data samples and updating cluster centers to optimize the search path of the cluster center. In addition, we propose a new evaluation index named the Coefficient of Variation Metric (CVM) to evaluate metric effectiveness. The dynamic metric accelerated method, whose complexity remains unchanged, can effectively accelerate the iteration speed of fuzzy clustering. The comparisons between the algorithm using the dynamic metric accelerated method and the corresponding algorithm on UCI, business district and COVID-19 CT image datasets show the superiority of the dynamic metric accelerated method in accelerating effect and clustering performance.