Communities in Data

Document Type

Presentation Abstract

Presentation Date

3-15-2021

Abstract

Although clustering is a crucial component of human experience, there are relatively few methods which harness the richness of a social perspective. Here, we introduce a probabilistically-interpretable measure of local depth from which the cohesion between points can be obtained, via partitioning. The PaLD approach allows one to obtain graph-type community structure (with resulting clusters) in a holistic manner which accounts for varying density and is entirely free of extraneous inputs (e.g., number of communities, neighborhood size, optimization criteria, etc.). Some theoretical properties of cohesion are included. Joint work with Kenneth Berenhaut.

Additional Details

March 15, 2021 at 3:00 p.m. via Zoom

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