Dynamic density-based clustering algorithm over uncertain data streams

In recent years, the uncertain data stream which is related in many real applications attracts more and more attention of researchers. As one aspect of uncertain character, existence-uncertainty can affect the clustering process and results significantly. The lately reported clustering algorithms are all based on K-Means algorithm with the inhere shortage. DCUStream algorithm which is density-based clustering algorithm over uncertain data stream is proposed in this paper. It can find arbitrary shaped clusters with less time cost in high dimension data stream. In the meantime, a dynamic density threshold is designed to accommodate the changing density of grids with time in data stream. The experiment results show that DCUStream algorithm can acquire more accurate clustering result and execute the clustering process more efficiently on progressing uncertain data stream.

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