An experimental evaluation of incremental and hierarchical k-median algorithms

Authors:
Chandrashekhar Nagarajan;David P. Williamson
Affiliations:
Yahoo! Inc., Sunnyvale, CA;Cornell University, Ithaca, NY
Venue:
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
Year:
2011

Citing 7
Cited 0

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
The Online Median Problem

SIAM Journal on Computing
Greedy facility location algorithms analyzed using dual fitting with factor-revealing LP

Journal of the ACM (JACM)
Local Search Heuristics for k-Median and Facility Location Problems

SIAM Journal on Computing
Approximation algorithms for hierarchical location problems

Journal of Computer and System Sciences - Special issue on network algorithms 2005
Incremental Medians via Online Bidding

Algorithmica
A General Approach for Incremental Approximation and Hierarchical Clustering

SIAM Journal on Computing

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this paper, we consider different incremental and hierarchical k-median algorithms with provable performance guarantees and compare their running times and quality of output solutions on different benchmark k-median datasets. We determine that the quality of solutions output by these algorithms for all the datasets is much better than their performance guarantees suggest. Since some of the incremental k-median algorithms require approximate solutions for the k-median problem, we also compare some of the existing k-median algorithms' running times and quality of solutions obtained on these datasets.