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AAAI
2006

When Is Constrained Clustering Beneficial, and Why?

14 years 26 days ago
When Is Constrained Clustering Beneficial, and Why?
Several researchers have illustrated that constraints can improve the results of a variety of clustering algorithms. However, there can be a large variation in this improvement, even for a fixed number of constraints for a given data set. We present the first attempt to provide insight into this phenomenon by characterizing two constraint set properties: inconsistency and incoherence. We show that these measures are strongly anti-correlated with clustering algorithm performance. Since they can be computed prior to clustering, these measures can aid in deciding which constraints to use in practice.
Kiri Wagstaff, Sugato Basu, Ian Davidson
Added 30 Oct 2010
Updated 30 Oct 2010
Type Conference
Year 2006
Where AAAI
Authors Kiri Wagstaff, Sugato Basu, Ian Davidson
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