Google TechTalks March 22, 2006 Aristides Gionis Aristides Gionis received his Ph.D. from Stanford University in 2003, and he is currently a senior researcher at the Basic Research Unit of Helsinki Institute of Information Technology. ABSTRACT We consider the following problem: given a set of clusterings, find a clustering that agrees as much as possible with the given clusterings. This problem, clustering aggregation, appears naturally in various contexts. For example,clustering categorical data is an instance of the problem: each categorical variable can be viewed as a clustering of the input rows. Moreover, clustering aggregation can be used as a metaclustering method to improve the robustness of clusterings. The problem formulation does not require a priori information about the number of clusters, and it gives a natural way for handlingmissing values. We give a formal statement of the clustering-aggregation problem, we discuss related work, and we suggest a number of algorithms. For several of the methods we provide theoretical guarantees on the quality of the solutions. We also show how sampling can be used to scale the algorithms for large data sets. We give an extensive empirical evaluation demonstrating the usefulness of the problem and of the solutions. Joint work with Panayiotis Tsaparas and Heikki Mannila.