Measures of Clustering Quality: A Working Set of Axioms for Clustering
Ben-David, Shai, Ackerman, Margareta
–Neural Information Processing Systems
Aiming towards the development of a general clustering theory, we discuss abstract axiomatization for clustering. In this respect, we follow up on the work of Kelinberg, (Kleinberg) that showed an impossibility result for such axiomatization. We argue that an impossibility result is not an inherent feature of clustering, but rather, to a large extent, it is an artifact of the specific formalism used in Kleinberg. As opposed to previous work focusing on clustering functions, we propose to address clustering quality measures as the primitive object to be axiomatized. We show that principles like those formulated in Kleinberg's axioms can be readily expressed in the latter framework without leading to inconsistency.
Neural Information Processing Systems
Feb-15-2020, 01:11:53 GMT
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