Comparing Whole-space Clusterings

April 2 (Thursday) at 12 noon
Baker Hall 232 M

Speaker: José Chacón

Abstract: A population clustering can be understood as an essential partition of the support of a probability distribution. Different notions of cluster lead to different concepts of ideal population clusters, but no matter what approach is taken, eventually the researcher needs to evaluate the performance of a clustering methodology by measuring the distance between a data-driven clustering and the ideal population goal. In this talk, two new distances are proposed for this aim, by extending well-known distances between sets to distances between clusterings.

We thank Microsoft Research for their gracious support.