Abstract
Modern data are often high dimensional and dynamic. Subspace clustering aims at finding the clusters and the dimensions of the high dimensional feature space where these clusters exist. So far, the subspace clustering methods are mainly static and cannot address the dynamic nature of modern data. In this paper, we propose a dynamic subspace clustering method, which extends the density based projected clustering algorithm PreDeCon for dynamic data. The proposed method efficiently examines only those clusters that might be affected due to the population update. Both single and batch updates are considered.
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Kriegel, HP., Kröger, P., Ntoutsi, I., Zimek, A. (2011). Density Based Subspace Clustering over Dynamic Data. In: Bayard Cushing, J., French, J., Bowers, S. (eds) Scientific and Statistical Database Management. SSDBM 2011. Lecture Notes in Computer Science, vol 6809. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22351-8_24
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DOI: https://doi.org/10.1007/978-3-642-22351-8_24
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