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.