Dagstuhl-Seminar — New Trends in Clustering

The study of clustering problems dates back several decades, and has led to the adaptation of versatile and powerful algorithm design techniques. In recent years, there has been a surge of activities in the study of clustering problems from different computational vantage points, leading to various new techniques for the development of approximation algorithms. These include new algorithm prototypes that are applicable to sublinear models, and geometric tools to deal with the challenges of high dimensionality. Each of these strands of research focuses on some challenge faced in modern applications involving massive and rapidly evolving data sets, and attempts to design clustering algorithms that overcome this challenge. This had led to a series of recent results on dynamic, online, streaming and massively parallel clustering, as well as on designing clustering algorithms whose outputs are fair, robust, and differentially private. The seminar aims to bring together researchers working on clustering problems from these divergent perspectives.