Local Connectivity in Centroid Clustering

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Clustering is a fundamental task in unsupervised learning, one that targets to group a dataset into clusters of similar objects. There has been recent interest in embedding normative considerations around fairness within clustering formulations. In this paper, we consider 'local connectivity' as a crucial factor in assessing membership desert in centroid clustering; we use local connectivity to refer to the support offered by the local neighborhood of an object towards supporting its membership to the cluster in question. We motivate the need to consider local connectivity of objects in cluster assignment, and provide ways to quantify local connectivity in a given clustering. We then exploit concepts from density-based clustering and devise LOFKM, a clustering method that seeks to deepen local connectivity in clustering outputs, while staying within the framework of centroid clustering. Through an empirical evaluation over real-world datasets, we illustrate that LOFKM achieves notable improvements in local connectivity at reasonable costs to clustering quality, establishing the effectiveness of the method.
Original languageEnglish
Title of host publicationIDEAS '20: Proceedings of the 24th Symposium on International Database Engineering & Applications
PublisherAssociation for Computing Machinery (ACM)
Publication statusPublished - Aug 2020
Event24th International Database Engineering & Applications Symposium -
Duration: 12 Aug 202018 Aug 2020


Conference24th International Database Engineering & Applications Symposium
Abbreviated titleIDEAS 2020
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