Abstract
While spectral clustering has recently shown great promise, computational cost makes it infeasible for use with large data sets. To address this computational challenge, this paper considers the problem of approximate spectral clustering, which enables both the feasibility (of approximately clustering in very large and unloadable data sets) and acceleration (of clustering in loadable data sets), while maintaining acceptable accuracy. We examine and propose several schemes for approximate spectral grouping, and make an empirical comparison of those schemes in combination with several sampling strategies. Experimental results on several synthetic and real-world data sets show that approximate spectral clustering can achieve both the goals of feasibility and acceleration.
This work was supported by ARC Discovery Project DP0663196.
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Wang, L., Leckie, C., Ramamohanarao, K., Bezdek, J. (2009). Approximate Spectral Clustering. In: Theeramunkong, T., Kijsirikul, B., Cercone, N., Ho, TB. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2009. Lecture Notes in Computer Science(), vol 5476. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01307-2_15
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DOI: https://doi.org/10.1007/978-3-642-01307-2_15
Publisher Name: Springer, Berlin, Heidelberg
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