2004 AProbabilisticFrameworkForSemiSupClust

(Basu et al., 2004) ⇒ Sugato Basu, Mikhail Bilenko, and Raymond Mooney. (2004). “A Probabilistic Framework for Semi-Supervised Clustering.” In: Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2004). doi:10.1145/1014052.1014062

Subject Headings: Semi-Supervised Clustering, Pairwise Constraint.

Notes

Cited By

~370 http://scholar.google.com/scholar?q=%22A+Probabilistic+Framework+for+Semi-Supervised+Clustering%22+2004

Quotes

Abstract

Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clusters. In recent years, a number of algorithms have been proposed for enhancing clustering quality by employing such supervision. Such methods use the constraints to either modify the objective function, or to learn the distance measure. We propose a probabilistic model for semi-supervised clustering based on Hidden Markov Random Fields (HMRFs) that provides a principled framework for incorporating supervision into prototype-based clustering. The model generalizes a previous approach that combines constraints and Euclidean distance learning, and allows the use of a broad range of clustering distortion measures, including Bregman divergences (e.g., Euclidean distance and I-divergence) and directional similarity measures (e.g., cosine similarity). We present an algorithm that performs partitional semi-supervised clustering of data by minimizing an objective function derived from the posterior energy of the HMRF model. Experimental results on several text data sets demonstrate the advantages of the proposed framework.

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2004 AProbabilisticFrameworkForSemiSupClust	Sugato Basu Mikhail Bilenko Raymond J. Mooney			A Probabilistic Framework for Semi-Supervised Clustering		Proceedings of the tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining	http://dx.doi.org/10.1145/1014052.1014062	10.1145/1014052.1014062		2004