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.
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Tasoulis, S.K., Tasoulis, D.K., Plagianakos, V.P. (2012). Clustering of High Dimensional Data Streams. In: Maglogiannis, I., Plagianakos, V., Vlahavas, I. (eds) Artificial Intelligence: Theories and Applications. SETN 2012. Lecture Notes in Computer Science(), vol 7297. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30448-4_28
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DOI: https://doi.org/10.1007/978-3-642-30448-4_28
Publisher Name: Springer, Berlin, Heidelberg
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