2012 LocallyScaledSpectralClustering

• (Correa & Lindstrom, 2012) ⇒ Carlos D. Correa, and Peter Lindstrom. (2012). “Locally-scaled Spectral Clustering Using Empty Region Graphs.” In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2012). ISBN:978-1-4503-1462-6 doi:10.1145/2339530.2339736

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222012%22+Locally-scaled+Spectral+Clustering+Using+Empty+Region+Graphs
• http://dl.acm.org/citation.cfm?id=2339530.2339736&preflayout=flat#citedby

Quotes

Author Keywords

• Algorithms; data mining; proximity graphs; spectral clustering

Abstract

This paper introduces a new method for estimating the local neighborhood and scale of data points to improve the robustness of spectral clustering algorithms. We employ a subset of empty region graphs - the β-skeleton - and non-linear diffusion to define a locally-adapted affinity matrix, which, as we demonstrate, provides higher quality clustering than conventional approaches based on κ nearest neighbors or global scale parameters. Moreover, we show that the clustering quality is far less sensitive to the choice of β and other algorithm parameters, and to transformations such as geometric distortion and random perturbation. We summarize the results of an empirical study that applies our method to a number of 2D synthetic data sets, consisting of clusters of arbitrary shape and scale, and to real multi-dimensional classification examples from benchmarks, including image segmentation.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2012 LocallyScaledSpectralClustering	Carlos D. Correa Peter Lindstrom			Locally-scaled Spectral Clustering Using Empty Region Graphs				10.1145/2339530.2339736		2012