Approximation algorithms for facility location problems (extended abstract)
STOC '97 Proceedings of the twenty-ninth annual ACM symposium on Theory of computing
Greedy strikes back: improved facility location algorithms
Journal of Algorithms
Clustering to minimize the sum of cluster diameters
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
Local search heuristic for k-median and facility location problems
STOC '01 Proceedings of the thirty-third annual ACM symposium on Theory of computing
A new greedy approach for facility location problems
STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
A constant-factor approximation algorithm for the k-median problem
Journal of Computer and System Sciences - STOC 1999
Quick and good facility location
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data
WABI '02 Proceedings of the Second International Workshop on Algorithms in Bioinformatics
Approximation algorithms for facility location problems
APPROX '00 Proceedings of the Third International Workshop on Approximation Algorithms for Combinatorial Optimization
Improved Approximation Algorithms for Metric Facility Location Problems
APPROX '02 Proceedings of the 5th International Workshop on Approximation Algorithms for Combinatorial Optimization
SIAM Journal on Computing
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Optimal Time Bounds for Approximate Clustering
Machine Learning
Incremental Clustering and Dynamic Information Retrieval
SIAM Journal on Computing
Performance guarantees for hierarchical clustering
Journal of Computer and System Sciences - Special issue on COLT 2002
An experimental evaluation of incremental and hierarchical k-median algorithms
SEA'11 Proceedings of the 10th international conference on Experimental algorithms
A General Approach for Incremental Approximation and Hierarchical Clustering
SIAM Journal on Computing
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We formulate and (approximately) solve hierarchical versions of two prototypical problems in discrete location theory, namely, the metric uncapacitated k-median and facility location problems. Our work yields new insights into hierarchical clustering, a widely used technique in data analysis. For example, we show that every metric space admits a hierarchical clustering that is within a constant factor of optimal at every level of granularity with respect to the average (squared) distance objective. A key building block of our hierarchical facility location algorithm is a constant-factor approximation algorithm for an ''incremental'' variant of the facility location problem; the latter algorithm may be of independent interest.