Constrained K-means Clustering with Background Knowledge

Constrained K-means Clustering with Background Knowledge (2001)

by Kiri Wagstaff , Claire Cardie , Seth Rogers , Stefan Schroedl

Venue:	In ICML
Citations:	250 - 9 self

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Datum	Value	Source
TITLE	Constrained K-means Clustering with Background Knowledge	user correction - Legacy Corrections
AUTHOR NAME	Kiri Wagstaff	user correction - Legacy Corrections
AUTHOR NAME	Claire Cardie	user correction - Legacy Corrections
AUTHOR AFFIL	Department of Computer Science, Cornell University, Ithaca	user correction - Legacy Corrections
AUTHOR ADDR	NY 14853 USA	user correction - Legacy Corrections
AUTHOR NAME	Seth Rogers	user correction - Legacy Corrections
AUTHOR NAME	Stefan Schroedl	user correction - Legacy Corrections
AUTHOR AFFIL	DaimlerChrysler Research and Technology Center	user correction - Legacy Corrections
AUTHOR ADDR	1510 Page Mill Road, Palo Alto, CA 94304 USA	user correction - Legacy Corrections
ABSTRACT	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 pro tably modi- ed to make use of this information. In experiments with arti cial 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. 1.	user correction - Legacy Corrections
YEAR	2001	INFERENCE
VENUE	In ICML	INFERENCE
VENUE TYPE	CONFERENCE	INFERENCE
PAGES	577--584	INFERENCE
VOLUME	2001	INFERENCE
CITATIONS	16 found	ParsCit 1.0