• Calendar

May 1, Wednesday
12:00 – 13:00

Clustering is one of the most widely used techniques for exploratory data analysis. Across many disciplines, people try to get intuition about their data by identifying meaningful groups among the data points. In the past five decades, many clustering algorithms have been developed and applied to a wide range of practical problems. However, in spite of the abundance of clustering research published every year, we are far from having satisfactory understanding of some of the most basic general issues in clustering. In this talk I survey some recent work on developing a general theory of clustering, aiming, among other things, to provide the clustering user with some systematic guidance concerning the matching between clustering algorithms (and their parameter settings) and specific clustering tasks. This talk is based on joint work with my past students, David, Pal, Rita Ackerman and David Loker.