Comparison of scalable fuzzy clustering methods

Fuzzy c-means (FCM) is a well-known algorithm for clustering data, but for large datasets termination takes significant time. As a result, a number of scalable algorithms based on FCM have been developed. In this paper, four scalable variants of FCM are compared to the base algorithm. Runtime and three quality metrics are calculated. Experimental results using five data sets are analyzed. We show that the scalable algorithms are consistent with regard to speedup, but less consistent when quality is considered. The three quality measures are shown to have little correlation and vary in magnitude across datasets. Selection of a scalable algorithm must consider a tradeoff between the quality of results and speed. Of the variants, single pass FCM (SPFCM) is fastest with good fidelity to FCM, and extensible fast FCM (eFFCM) is almost as fast as SPFCM (as implemented) with very good fidelity to FCM. Random FCM is the fastest overall and often close in quality to FCM. The results showed that scalable algorithms occasionally produce better optimized results than FCM.