Title:	Meta Clustering
Authors:	Caruana, Rich Artigas, Pedro Goldenberg, Ann Likhodedov, Anton
Keywords:	computer science technical report
Issue Date:	8-Nov-2002
Publisher:	Cornell University
Citation:	http://techreports.library.cornell.edu:8081/Dienst/UI/1.0/Display/cul.cs/TR2002-1884
Abstract:	Most clustering methods search for one optimal partitioning of the data. Often it is better to search for many different clusterings of the data and present the user with a means of efficiently navigating between them. We present two algorithms for generating many alternate clusterings: Sample-and-Merge and Component Reweighting. We then use clustering at a meta level to organize these different base-level clusterings. This {\em MetaClustering} partitions the base-level clusterings into groups of similar clusterings. We demonstrate MetaClustering on a synthetic data set, and on a real protein data set. The results show that the algorithms are effective at generating qualitatively different clusterings, and at organizing these clusterings so that similar ones are grouped together.
URI:	http://hdl.handle.net/1813/5860
Appears in Collections:	Computer Science Technical Reports

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