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Clustering to the Fewest Clusters Under Intra-Cluster Dissimilarity Constraints

Abstract : This paper introduces the equiwide clustering problem, where valid partitions must satisfy intra-cluster dissimilarity constraints. Unlike most existing clustering algorithms, equiwide clustering relies neither on density nor on a predefined number of expected classes, but on a dissimilarity threshold. Its main goal is to ensure an upper bound on the error induced by ultimately replacing any object with its cluster representative. Under this constraint, we then primarily focus on minimizing the number of clusters, along with potential sub-objectives. We argue that equiwide clustering is a sound clustering problem, and discuss its relationship with other optimization problems, existing and novel implementations as well as approximation strategies. We review and evaluate suitable clustering algorithms to identify trade-offs between the various practical solutions for this clustering problem.
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Contributor : Brice Chardin Connect in order to contact the contributor
Submitted on : Monday, September 27, 2021 - 4:21:36 PM
Last modification on : Wednesday, October 20, 2021 - 3:22:25 AM


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  • HAL Id : hal-03356000, version 1
  • ARXIV : 2109.13644


Jennie Andersen, Brice Chardin, Mohamed Tribak. Clustering to the Fewest Clusters Under Intra-Cluster Dissimilarity Constraints. Proceedings of the 33rd IEEE International Conference on Tools with Artificial Intelligence, Nov 2021, Athens, Greece. ⟨hal-03356000⟩



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