Evaluating scalable fuzzy clustering

Clustering large data has the problem of not having all the data fit in the memory at one time. It is a challenge to apply fuzzy clustering algorithms to get a partition in a timely manner. In this paper, we compare the online fuzzy clustering and single pass fuzzy clustering algorithms, which can be used to cluster very large data sets which might be treated as streaming data, with fuzzy c-means. We introduce more meaningful partition comparison measurements based on cluster center location instead of using the difference in R_m value. We obtained results on several large volumes of magnetic resonance images which indicate that the online FCM algorithm produces partitions which are very close to what you could get if you clustered all the data at one time. We also show online FCM outperforms single pass FCM and it can process streaming data as it comes without degradation in most cases.