Clustering with Propagated Constraints
作者： Eric Robert Eaton
DOI:
关键词: Constrained clustering 、 Pairwise comparison 、 Cluster analysis 、 Quality (business) 、 Focus (optics) 、 Theoretical computer science 、 Gaussian function 、 Cluster (physics) 、 Computer science 、 Perspective (graphical)
摘要: Title of Thesis: Clustering with Propagated Constraints Eric Robert Eaton, Master Science, 2005 Thesis directed by: Dr. Marie desJardins, Assistant Professor Department Computer Science and Electrical Engineering Background knowledge in the form constraints can dramatically improve quality generated clustering models. In constrained clustering, these typically specify relative cluster membership pairs points. They are tedious to expensive from a user perspective, yet very useful large quantities. Existing methods perform well when given quantities constraints, but do not focus on performing small This thesis focuses providing high-quality constraints. It proposes method for propagating pairwise nearby instances using Gaussian function. takes few easily specified propagates them points constrain local neighborhood. propagated yield superior performance fewer than only original user-specified The experiments compare that established algorithms several real-world data sets.
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