Performance Comparison of Two Streaming Data Clustering Algorithms

The weighted fuzzy c-mean clustering algorithm (WFCM) and weighted fuzzy c-mean-adaptive cluster number (WFCM-AC) are extension of traditional fuzzy c-mean algorithm to stream data clustering algorithm. Clusters in WFCM are generated by renewing the centers of weighted cluster by iteration. On the other hand, WFCM-AC generates clusters by applying WFCM on the data & selecting best K± initialize center. In this paper we have compared these two methods using KDD-CUP’99 data set. We have compared these algorithms with respect to number of valid clusters, computational time and mean standard error. Keywords—Streaming data, weighted fuzzy c-mean, weighted fuzzy c-mean-adaptive clustering.