Section: New Results

Weight-based search to find clusters around medians in subspaces

participants: C Rigotti, G Beslon

There exist several clustering paradigms, leading to different techniques that are complementary in the analyst toolbox, each having its own merits and interests. Among these techniques, the K-medians approach is recognized as being robust to noise and outliers, and is an important optimization task with many different applications (e.g., facility location). In the context of subspace clustering, several paradigms have been investigated (e.g., centroid-based, cell-based), while the median-based approach has received less attention. Moreover, using standard subspace clustering outputs (e.g., centroids, medoids) there is no straightforward procedure to compute the cluster membership that optimizes the dispersion around medians. We advocated for the use of median-based subspace clustering as a complementary tool. Indeed, we showed that such an approach exhibits satisfactory quality clusters when compared to well-established paradigms, while medians have still their own interests depending on the user application (robustness to noise/outliers and location optimality). We showed that a weight-based hill climbing algorithm using a stochastic local exploration step can be sufficient to produce the clusters.

This research has been published in the proceedings of the ACM-SAC conference (Pau, March 2018) where it received the best paper award [26].