Abstract
In this paper, we propose a subspace clustering method based on compressibility. It is widely accepted that compressibility is deeply related to inductive learning. We have come to believe that compressibility is promising as an evaluation criterion in subspace clustering, and propose SUBCCOM in order to verify this belief. Experimental evaluation employs both artificial and real data sets.
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Narahashi, M., Suzuki, E. (2002). Subspace Clustering Based on Compressibility. In: Lange, S., Satoh, K., Smith, C.H. (eds) Discovery Science. DS 2002. Lecture Notes in Computer Science, vol 2534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36182-0_46
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DOI: https://doi.org/10.1007/3-540-36182-0_46
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Online ISBN: 978-3-540-36182-4
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