2015 SpectralEnsembleClustering

• (Liu et al., 2015) ⇒ Hongfu Liu, Tongliang Liu, Junjie Wu, Dacheng Tao, and Yun Fu. (2015). “Spectral Ensemble Clustering.” In: Proceedings of the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2015). ISBN:978-1-4503-3664-2 doi:10.1145/2783258.2783287

Subject Headings:

Cited By

• http://scholar.google.com/scholar?q=%222015%22+Spectral+Ensemble+Clustering
• http://dl.acm.org/citation.cfm?id=2783258.2783287&preflayout=flat#citedby

Quotes

Author Keywords

• Co-association matrix; data mining; ensemble clustering; k-means

Abstract

Ensemble clustering, also known as consensus clustering, is emerging as a promising solution for multi-source and/or heterogeneous data clustering. The co-association matrix based method, which redefines the ensemble clustering problem as a classical graph partition problem, is a landmark method in this area. Nevertheless, the relatively high time and space complexity preclude it from real-life large-scale data clustering. We therefore propose SEC, an efficient Spectral Ensemble Clustering method based on co-association matrix. We show that SEC has theoretical equivalence to weighted K-means clustering and results in vastly reduced algorithmic complexity. We then derive the latent consensus function of SEC, which to our best knowledge is among the first to bridge co-association matrix based method to the methods with explicit object functions. The robustness and generalizability of SEC are then investigated to prove the superiority of SEC in theory. We finally extend SEC to meet the challenge rising from incomplete basic partitions, based on which a scheme for big data clustering can be formed. Experimental results on various real-world data sets demonstrate that SEC is an effective and efficient competitor to some state-of-the-art ensemble clustering methods and is also suitable for big data clustering.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2015 SpectralEnsembleClustering	Junjie Wu Dacheng Tao Hongfu Liu Tongliang Liu Yun Fu			Spectral Ensemble Clustering				10.1145/2783258.2783287		2015