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Hierarchical clustering beyond the worst-case

Abstract:

Hiererachical clustering, that is computing a recursive partitioning of a dataset to obtain clusters at increasingly finer granularity is a fundamental problem in data analysis. Although hierarchical clustering has mostly been studied through procedures such as linkage algorithms, or top-down heuristics, rather than as optimization problems, recently Dasgupta proposed an objective function for hierarchical clustering and initiated a line of work developing algorithms that explicitly optimize ...

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Publication status:
Published
Peer review status:
Peer reviewed
Version:
Accepted manuscript

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Authors


Cohen-Addad, V More by this author
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Department:
Oxford, MPLS, Computer Science
Mallmann-Trenn, F More by this author
Publisher:
Neural Information Processing Systems Publisher's website
Publication date:
2018-07-01
Acceptance date:
2017-09-04
Pubs id:
pubs:725786
URN:
uri:8447be4b-ee2f-421f-85e0-a81764d9681b
UUID:
uuid:8447be4b-ee2f-421f-85e0-a81764d9681b
Local pid:
pubs:725786

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