3 accepted papers
Evaluating the quality of a clustering result without access to ground truth labels is fundamental for research in data mining. However, most cluster validation indices (CVIs) do not consider the noise assignments by density-based clustering methods like DBSCAN or HDBSCAN, even though the ability to…
Fairness in data mining tasks like clustering has recently become an increasingly important aspect. However, few clustering algorithms exist that focus on fair groupings of data with sensitive attributes. Including fairness in the clustering objective is especially hard for density-based clusterin…
Hierarchical clustering is a powerful tool for exploratory data analysis, organizing data into a tree of clusterings from which a partition can be chosen. This paper generalizes these ideas by proving that, for any reasonable hierarchy, one can optimally solve any center-based clustering objective o…