Performance Comparison of Two Streaming Data Clustering Algorithms

Abstract

Abstract-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.

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“…It uses a decaying cluster structure with a histogram to approximate the streaming data. Although the algorithm has the disadvantage of needing an expert intervention to specify many parameters before it works, its performance is better than HPStream algorithm [27].…”