2009 APrincipledandFlexibleFramework

• (Qi et al., 2009) ⇒ ZiJie Qi, and Ian Davidson. (2009). “A Principled and Flexible Framework for Finding Alternative Clusterings.” In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD-2009). doi:10.1145/1557019.1557099

Subject Headings: Constrained Clustering.

Cited By

• http://scholar.google.com/scholar?q=%22A+principled+and+flexible+framework+for+finding+alternative+clusterings%22+2009
• http://portal.acm.org/citation.cfm?doid=1557019.1557099&preflayout=flat#citedby

Quotes

Abstract

The aim of data mining is to find novel and actionable insights in data. However, most algorithms typically just find a single (possibly non-novel/actionable) interpretation of the data even though alternatives could exist. The problem of finding an alternative to a given original clustering has received little attention in the literature. Current techniques (including our previous work) are unfocused/unrefined in that they broadly attempt to find an alternative clustering but do not specify which properties of the original clustering should or should not be retained. In this work, we explore a principled and flexible framework in order to find alternative clusterings of the data. The approach is principled since it poses a constrained optimization problem, so its exact behavior is understood. It is flexible since the user can formally specify positive and negative feedback based on the existing clustering, which ranges from which clusters to keep (or not) to making a trade-off between alternativeness and clustering quality.

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References

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	Author	volume	Date Value	title	type	journal	titleUrl	doi	note	year
2009 APrincipledandFlexibleFramework	Ian Davidson ZiJie Qi			A Principled and Flexible Framework for Finding Alternative Clusterings		KDD-2009 Proceedings		10.1145/1557019.1557099		2009