Seminar: Clustering Functional Data using Projection

Submitted by admin on Fri, 10/12/2018 - 12:33
  • Speaker: Dr. Pham Huy Tung, Department of Mathematics and Statistics, The University of Melbourne
  • Time: 8:30, Friday, October 12, 2018
  • Place: Data Science Laboratory, T5 Building, P.502, Hanoi University of Science, 334 Nguyen Trai, Thanh Xuan, Hanoi
  • Summary: We show that, in the functional data context, by appropriately exploiting the functional nature of the data, it is possible to cluster the observations asymptotically perfectly. We demonstrate that this level of performance can sometimes be achieved by the k-means algorithm as long as the data are projected on 1 dimensional space. In general, the notion of ideal cluster is not clearly defined. We derive our results in the setting where the data come from two populations whose distributions differ at least in terms of means, and where an ideal cluster corresponds to one of these two populations. We propose an iterative algorithm to choose the projection functions in a way that optimises clustering performance.  We apply our iterative clustering procedure on simulated and real data, where we show that it works well.