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Clustering of High Dimensional Data Streams
Sotiris K. Tasoulis1, Dimirtis K. Tasoulis2, and Vassilis P. Plagianakos1
1Department of Computer Science and Biomedical Informatics, University of Central Greece, Papassiopoulou 2–4, Lamia, 35100, Greece
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2Winton Capital Management, 1–5 St Mary Abbot’s Place, SW8 6LS, United Kingdom
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Abstract. Clustering of data streams has become a task of great interest in the recent years as such data formats is are becoming increasingly ambiguous. In many cases, these data are also high dimensional and in result more complex for clustering. As such there is a growing need for algorithms that can be applied on streaming data and the at same time can cope with high dimensionality. To this end, here we design a streaming clustering approach by extending a recently proposed high dimensional clustering algorithm.
Keywords: Clustering, Data Streams, Kernel Density Estimation, Incremental Principal Component Analysis
LNAI 7297, p. 223 ff.
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© Springer-Verlag Berlin Heidelberg 2012