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HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 267Data Stream Clustering Algorithm Based on Affinity...

Data Stream Clustering Algorithm Based on Affinity Propagation and Density

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Abstract:

Data stream clustering is an important issue in data steam mining. In the field of data stream analysis, conventional methods seem not quite efficient. Because neither they can adapt to the dynamic environment of data stream, nor the mining models and result s can meet users’ needs. An affinity propagation and grid based clustering method is proposed to effectively address the problem. The algorithm applies AP clustering on each partition of the data stream to generate reference point set, and subsequently density based clustering is applied to these reference points to get the clustering result of each periods. Theoretic analysis and experimental results show it is effective and efficient.

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Periodical:

Advanced Materials Research (Volume 267)

Pages:

444-449

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.267.444

Citation:

Cite this paper

Online since:

June 2011

Authors:

Yang Li, Bai Hong Tan

Keywords:

Affinity Propagation, Data Stream, Density Based Clustering

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© 2011 Trans Tech Publications Ltd. All Rights Reserved

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