2011 ApproximateKernelKMeansSolution

• (Chitta et al., 2011) ⇒ Radha Chitta, Rong Jin, Timothy C. Havens, and Anil K. Jain. (2011). “Approximate Kernel K-means: Solution to Large Scale Kernel Clustering.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2011) Journal. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020558

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222011%22+Approximate+Kernel+K-means%3A+Solution+to+Large+Scale+Kernel+Clustering
• http://dl.acm.org/citation.cfm?id=2020408.2020558&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; clustering; k-means; kernel clustering; large-scale clustering; performance

Abstract

Digital data explosion mandates the development of scalable tools to organize the data in a meaningful and easily accessible form. Clustering is a commonly used tool for data organization. However, many clustering algorithms designed to handle large data sets assume linear separability of data and hence do not perform well on real world data sets. While kernel-based clustering algorithms can capture the non-linear structure in data, they do not scale well in terms of speed and memory requirements when the number of objects to be clustered exceeds tens of thousands. We propose an approximation scheme for kernel k-means, termed approximate kernel k-means, that reduces both the computational complexity and the memory requirements by employing a randomized approach. We show both analytically and empirically that the performance of approximate kernel k-means is similar to that of the kernel k-means algorithm, but with dramatically reduced run-time complexity and memory requirements.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2011 ApproximateKernelKMeansSolution	Rong Jin Anil K. Jain Radha Chitta Timothy C. Havens			Approximate Kernel K-means: Solution to Large Scale Kernel Clustering				10.1145/2020408.2020558		2011