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Authors: N. C. Ruckiya Sinorina ; Howard J. Hamilton and Sandra Zilles

Affiliation: Department of Computer Science, University of Regina, 3737 Wascana Parkway, Regina, SK, Canada

Keyword(s): Consensus Clustering, Association Matrix.

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.

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Paper citation in several formats:
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; ISSN 2184-433X, SciTePress, pages 326-335. DOI: 10.5220/0010820800003116

@conference{icaart22,
author={N. C. Ruckiya Sinorina. and Howard J. 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},
issn={2184-433X},
}

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
IS - 2184-433X
AU - Sinorina, N.
AU - Hamilton, H.
AU - Zilles, S.
PY - 2022
SP - 326
EP - 335
DO - 10.5220/0010820800003116
PB - SciTePress

- Science and Technology Publications, Lda.
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