Title: Multiclustering: Avoiding the Natural Shape of Underlying Metrics.

Speaker: Prof. Daniel Ashlock, University of Guelph

Abstract:

    This talk introduces a novel clustering technique that exploits the
variability of k-means clustering to grant immunity to the cluster shapes
that are artifacts of the distance measure used rather than a feature of the
data.  The talk assumes that that the listener is a nonspecialist with
potential applications for clustering algorithms. In addition to clustering
data, multiclustering gives an advisory as the natural number of clusters in
the data (or an indication that no such natural clusters exist).