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.

• Type: Conference
• Year: 2001
• Where: ICML
• Authors: Kiri Wagstaff, Claire Cardie, Seth Rogers, Stefan Schrödl
• Keywords: Data Instances, GPS Data, ICML 2001, K-means Clustering Algorithm, Machine Learning