Hierarchical clustering : Objective functions and algorithms
- Submitting institution
-
King's College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 127986522
- 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
- Deposit exception
- 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
- The paper was the first to generalise Dasgupta's celebrated cost function for hierarchical clustering. It proves the best-known approximation ratio for the Dasgupta cost function. This journal version builds on the contributions presented at SODA18 and NIPS17. This introduced ideas and techniques that have been taken up by the algorithmic community, including: KDD19 (Monath et al.), SODA19 (Charikar et al.), NIPS17 (Moseley and Wang) as well as in other fields: OCEANS18 (Dutt et al.) and MINERALS20 (Xu et al.).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -