A constant-factor approximation algorithm for the k-median problem (extended abstract)
STOC '99 Proceedings of the thirty-first annual ACM symposium on Theory of computing
Approximate clustering via core-sets
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A local search approximation algorithm for k-means clustering
Proceedings of the eighteenth annual symposium on Computational geometry
An Efficient k-Means Clustering Algorithm: Analysis and Implementation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast approximations for sums of distances, clustering and the Fermat--Weber problem
Computational Geometry: Theory and Applications
Group Nearest Neighbor Queries
ICDE '04 Proceedings of the 20th International Conference on Data Engineering
Aggregate Nearest Neighbor Queries in Road Networks
IEEE Transactions on Knowledge and Data Engineering
Aggregate nearest neighbor queries in spatial databases
ACM Transactions on Database Systems (TODS)
Two ellipse-based pruning methods for group nearest neighbor queries
Proceedings of the 13th annual ACM international workshop on Geographic information systems
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A Nearly Linear-Time Approximation Scheme for the Euclidean $k$-Median Problem
SIAM Journal on Computing
Computational Geometry: Theory and Applications
Tree-based partition querying: a methodology for computing medoids in large spatial datasets
The VLDB Journal — The International Journal on Very Large Data Bases
NP-hardness of Euclidean sum-of-squares clustering
Machine Learning
Flexible aggregate similarity search
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
A unified framework for approximating and clustering data
Proceedings of the forty-third annual ACM symposium on Theory of computing
IEEE Transactions on Knowledge and Data Engineering
Instant approximate 1-center on road networks via embeddings
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Hi-index | 0.00 |
In this paper, we study the k-clustering query problem on road networks, an important problem in Geographic Information Systems ("GIS"). Using previously developed Euclidean embeddings and reduction to fast nearest neighbor search, we show and analyze 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.