Formal Foundations of Clustering
Clustering is a central unsupervised learning task with a wide variety of applications. However, in spite of its popularity, it lacks a unified theoretical foundation. Recently, there has been work aimed at developing such a theory. We discuss recent advances in clustering theory, starting with results on clustering axioms.
We will then discuss a new framework for addressing one of the most prominent practical problems in the field, the selection of a clustering algorithm for a specific task. The framework rests on the identification of central properties capturing the input-output behaviour of clustering paradigms. We present several results in this direction, including a characterization of linkage-based clustering methods.
Margareta Ackerman is a postdoctoral fellow at UC San Diego specializing in formal foundations of clustering. She received her PhD from the University of Waterloo and is a winner of numerous awards, including the competitive NSERC award for Canadian postdoctoral fellows.
Margareta’s research interests include machine learning, game theory, bioinformatics, information retrieval, and automata theory. Among her research contributions is a consistent set of axioms of clustering quality measures, a characterization of linkage-based clustering, and a novel framework for selecting clustering algorithms based on users’ needs.