Genie+OWA: Robustifying hierarchical clustering with OWA-based linkages

We investigate the application of the Ordered Weighted Averaging (OWA) data fusion operator in agglomerative hierarchical clustering. The examined setting generalises the well-known single, complete and average linkage schemes. It allows to embody expert knowledge in the cluster merge process and to...

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Bibliographic Details
Published in:Information sciences Vol. 520; pp. 324 - 336
Main Authors: Cena, Anna, Gagolewski, Marek
Format: Journal Article
Language:English
Published: Elsevier Inc 01.05.2020
Subjects:
Aggregation
Data fusion
Genie
Hierarchical clustering
OWA
Aggregation
OWA
Data fusion
Genie
Hierarchical clustering
ISSN:0020-0255, 1872-6291
Online Access:Get full text
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Summary:We investigate the application of the Ordered Weighted Averaging (OWA) data fusion operator in agglomerative hierarchical clustering. The examined setting generalises the well-known single, complete and average linkage schemes. It allows to embody expert knowledge in the cluster merge process and to provide a much wider range of possible linkages. We analyse various families of weighting functions on numerous benchmark data sets in order to assess their influence on the resulting cluster structure. Moreover, we inspect the correction for the inequality of cluster size distribution – similar to the one in the Genie algorithm. Our results demonstrate that by robustifying the procedure with the Genie correction, we can obtain a significant performance boost in terms of clustering quality. This is particularly beneficial in the case of the linkages based on the closest distances between clusters, including the single linkage and its “smoothed” counterparts. To explain this behaviour, we propose a new linkage process called three-stage OWA which yields further improvements. This way we confirm the intuition that hierarchical cluster analysis should rather take into account a few nearest neighbours of each point, instead of trying to adapt to their non-local neighbourhood.
ISSN:0020-0255
1872-6291
DOI:10.1016/j.ins.2020.02.025