Accelerated Fuzzy C-Means Clustering Based on New Affinity Filtering and Membership Scaling

TL;DR

This paper introduces AMFCM, an accelerated fuzzy c-means clustering algorithm that uses new affinity filtering and membership scaling techniques to significantly reduce convergence time and improve efficiency.

Contribution

It proposes a novel combination of affinity filtering and membership scaling to speed up FCM clustering, especially in the mid-to-late stages.

Findings

AMFCM reduces iteration count by 80% on average.

The algorithm is faster and more effective than existing methods.

Experimental results confirm improved convergence and efficiency.

Abstract

Fuzzy C-Means (FCM) is a widely used clustering method. However, FCM and its many accelerated variants have low efficiency in the mid-to-late stage of the clustering process. In this stage, all samples are involved in the update of their non-affinity centers, and the fuzzy membership grades of the most of samples, whose assignment is unchanged, are still updated by calculating the samples-centers distances. All those lead to the algorithms converging slowly. In this paper, a new affinity filtering technique is developed to recognize a complete set of the non-affinity centers for each sample with low computations. Then, a new membership scaling technique is suggested to set the membership grades between each sample and its non-affinity centers to 0 and maintain the fuzzy membership grades for others. By integrating those two techniques, FCM based on new affinity filtering and membership scaling (AMFCM) is proposed to accelerate the whole convergence process of FCM. Many experimental results performed on synthetic and real-world data sets have shown the feasibility and efficiency of the proposed algorithm. Compared with the state-of-the-art algorithms, AMFCM is significantly faster and more effective. For example, AMFCM reduces the number of the iteration of FCM by 80% on average.