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Approximation algorithms for hierarchical location problems
2003
Proceedings of the thirty-fifth ACM symposium on Theory of computing - STOC '03
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
doi:10.1145/780542.780549
dblp:conf/stoc/Plaxton03
fatcat:y3rvflbzaravvljdkjwa6jy3ji