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Hierarchical clustering: Objective functions and algorithms

Abstract:

Hierarchical clustering is a recursive partitioning of a dataset into clusters at an increasingly finer granularity. Motivated by the fact that most work on hierarchical clustering was based on providing algorithms, rather than optimizing a specific objective, Dasgupta (2016) framed similarity-based hierarchical clustering as a combinatorial optimization problem, where a `good' hierarchical clustering is one that minimizes some cost function. He showed that this cost function has certain d...

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

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Publisher copy:
10.1137/1.9781611975031.26

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Institution:
University of Oxford
Division:
MPLS Division
Department:
Computer Science
Role:
Author
Publisher:
Society for Industrial and Applied Mathematics Publisher's website
Publication date:
2018-01-10
Acceptance date:
2017-09-27
DOI:
Pubs id:
pubs:735174
URN:
uri:b14a2ba4-7ca9-4335-917b-fed539a012b6
UUID:
uuid:b14a2ba4-7ca9-4335-917b-fed539a012b6
Local pid:
pubs:735174

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