Efficient Removal of Weak Associations in Consensus Clustering

N. Sinorina, Howard Hamilton, Sandra Zilles

2022

Abstract

Consensus clustering methods measure the strength of an association between two data objects based on how often the objects are grouped together by the base clusterings. However, incorporating weak associations in the consensus process can have a negative effect on the quality of the aggregated clustering. This paper presents an efficient automatic approach for removing weak associations during the consensus process. We compare our approach to a brute force method used in an existing consensus function, NegMM, which tends to be rather inefficient in terms of runtime. Our empirical analysis on multiple datasets shows that the proposed approach produces consensus clusterings that are comparable in quality to the ones produced by the original NegMM method, yet at a much lower computational cost.

Paper Citation

in Harvard Style

Sinorina N., Hamilton H. and Zilles S. (2022). Efficient Removal of Weak Associations in Consensus Clustering. In Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART, ISBN 978-989-758-547-0, pages 326-335. DOI: 10.5220/0010820800003116

in Bibtex Style

@conference{icaart22,
author={N. Sinorina and Howard Hamilton and Sandra Zilles},
title={Efficient Removal of Weak Associations in Consensus Clustering},
booktitle={Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,},
year={2022},
pages={326-335},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010820800003116},
isbn={978-989-758-547-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 14th International Conference on Agents and Artificial Intelligence - Volume 3: ICAART,
TI - Efficient Removal of Weak Associations in Consensus Clustering
SN - 978-989-758-547-0
AU - Sinorina N.
AU - Hamilton H.
AU - Zilles S.
PY - 2022
SP - 326
EP - 335
DO - 10.5220/0010820800003116