Fast k-clustering Queries on Road Networks

In this article, we study the k-clustering query problem on road networks, an important problem in Geographic Information Systems. Using Euclidean embeddings and reduction to fast nearest neighbor search, we devise approximation algorithms for these problems. Since these problems are difficult to solve exactly – and even hard to approximate for most variants – we compare our constant factor approximation algorithms to exact answers on small synthetic datasets and on a dataset representing Tallahassee, Florida, a small city. We have implemented a web application that demonstrates our method for road networks in the same small city. Keywords-k-clustering, k-means, k-medians, k-centers, embeddings, Computational Geometry, GIS.