Hierarchical Clustering: Objective Functions and Algorithms
- Submitting institution
-
University of Oxford
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2083
- Type
- D - Journal article
- DOI
-
10.1145/3321386
- Title of journal
- Journal of the ACM
- Article number
- 26
- First page
- -
- Volume
- 66
- Issue
- 4
- ISSN
- 0004-5411
- Open access status
- Compliant
- Month of publication
- June
- Year of publication
- 2019
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
3
- Research group(s)
-
-
- Citation count
- 13
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- Hierarchical clustering was framed as a combinatorial optimization problem for the first time in a recent paper by Dasgupta (2016). This work considers an axiomatic way to identify meaningful objective functions for this combinatorial optimization problem. Several approximation algorithms (including the current best one) are also provided. Furthermore, the work introduces a framework to analyse hierarchical clustering outside of the worst-case setting which is more relevant for machine learning applications.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
