Scalable Distributed Approximation of Internal Measures for Clustering Evaluation

1 code implementation • 3 Mar 2020 • Federico Altieri, Andrea Pietracaprina, Geppino Pucci, Fabio Vandin

The experiments provide evidence that, unlike other heuristics, our estimation strategy not only provides tight theoretical guarantees but is also able to return highly accurate estimations while running in a fraction of the time required by the exact computation, and that its distributed implementation is highly scalable, thus enabling the computation of internal measures for very large datasets for which the exact computation is prohibitive.

Clustering