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ICML
2001
IEEE

Constrained K-means Clustering with Background Knowledge

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Constrained K-means Clustering with Background Knowledge
Clustering is traditionally viewed as an unsupervised method for data analysis. However, in some cases information about the problem domain is available in addition to the data instances themselves. In this paper, we demonstrate how the popular k-means clustering algorithm can be profitably modified to make use of this information. In experiments with artificial constraints on six data sets, we observe improvements in clustering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and observe dramatic increases in performance.
Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan
Added 17 Nov 2009
Updated 17 Nov 2009
Type Conference
Year 2001
Where ICML
Authors Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schrödl
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