Post-Education-for-All and Sustainable Development Paradigm: Structural Change and Diversifying Actors and Norms

Abstract

Clustering is traditionally viewed as an un- supervised method for data analysis. How- ever, 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 modi- fied to make use of this information. In ex- periments with artificial constraints on six data sets, we observe improvements in clus- tering accuracy. We also apply this method to the real-world problem of automatically detecting road lanes from GPS data and ob- serve dramatic increases in performance.