  We explore the geometrical interpretation of the PCA based clustering
algorithm Principal Direction Divisive Partitioning (PDDP). We give several
examples where this algorithm breaks down, and suggest a new method, gap
partitioning, which takes into account natural gaps in the data between
clusters. Geometric features of the PCA space are derived and illustrated and
experimental results are given which show our method is comparable on the
datasets used in the original paper on PDDP.
