Bisecting K-means and PDDP: A Comparative Analysis
Sergio M. Savaresi, Daniel L. Boley · University of Minnesota Digital Conservancy (University of Minnesota) · 2000
This paper deals with the problem of clustering a data-set. In particular, the bisecting divisive partitioning approach is here considered. We focus on two algorithms: the celebrated K-means algorithm, and the recently proposed Principal Direction Divisive Partitioning (PDDP) algorithm. A comparison of the two algorithms is given, under the assumption that the data set is uniformly distributed within an ellipsoid. In particular, the dynamic behavior of the K-means iterative procedure is studied; for the 2-dimensional case a closed-form model is given.