Abstract
Multi-criterion algorithms for clustering have gained some traction due to their ability to cater to a diverse range of cluster properties. Here, we investigate the interaction between the clustering criteria employed in a multi-objective algorithm and the distance functions on which these criteria operate. We do so by contrasting the multi-criterion evolutionary algorithm \(\varDelta \)-MOCK with a bi-objective version of the evolutionary multi-view clustering approach MVMC, which uses a single clustering criterion but can incorporate multiple dissimilarity matrices. Using a benchmark suite representing a diverse range of cluster properties, we illustrate that comparable results to \(\varDelta \)-MOCK can be achieved using MVMC with two complementary distance functions. We then establish the mathematical equivalence of \(\varDelta \)-MOCK’s connectivity objective to a compactness criterion operating on a redefined distance function. We conclude by discussing the implications of our findings for future work on the representation of clusters in multi-objective evolutionary clustering.
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Notes
- 1.
In supervised multi-view learning, a feature representation of each view is required, as the end-goal is typically a feature-to-output mapping.
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José-García, A., Handl, J. (2021). On the Interaction Between Distance Functions and Clustering Criteria in Multi-objective Clustering. In: Ishibuchi, H., et al. Evolutionary Multi-Criterion Optimization. EMO 2021. Lecture Notes in Computer Science(), vol 12654. Springer, Cham. https://doi.org/10.1007/978-3-030-72062-9_40
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