2009 ImprovingClusteringStabilitywit

• (Bekkerman et al., 2009) ⇒ Ron Bekkerman, Martin Scholz, and Krishnamurthy Viswanathan. (2009). “Improving Clustering Stability with Combinatorial MRFs.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557037

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Cited By

• http://scholar.google.com/scholar?q=%22Improving+clustering+stability+with+combinatorial+MRFs%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557037&preflayout=flat#citedby

Quotes

Author Keywords

Clustering Stability, Combinatorial MRF, Comraf

Abstract

As clustering methods are often sensitive to parameter tuning, obtaining stability in clustering results is an important task. In this work, we aim at improving clustering stability by attempting to diminish the influence of algorithmic inconsistencies and enhance the signal that comes from the data. We propose a mechanism that takes m clusterings as input and outputs [math]\displaystyle{ m }[/math] clusterings of comparable quality, which are in higher agreement with each other. We call our method the Clustering Agreement Process (CAP). To preserve the clustering quality, CAP uses the same optimization procedure as used in clustering. In particular, we study the stability problem of randomized clustering methods (which usually produce different results at each run). We focus on methods that are based on inference in a combinatorial Markov Field (or Comraf, for short) of a simple topology. We instantiate CAP as inference within a more complex, bipartite Comraf. We test the resulting system on four datasets, three of which are medium-sized text collections, while the fourth is a large-scale user/movie dataset. First, in all the four cases, our system significantly improves the clustering stability measured in terms of the macro-averaged Jaccard index. Second, in all the four cases our system managed to significantly improve clustering quality as well, achieving the state-of-the-art results. Third, our system significantly improves stability of consensus clustering built on top of the randomized clustering solutions.

References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 ImprovingClusteringStabilitywit				Improving Clustering Stability with Combinatorial MRFs				10.1145/1557019.1557037