  We study the use of power weighted shortest path distance functions for
clustering high dimensional Euclidean data, under the assumption that the data
is drawn from a collection of disjoint low dimensional manifolds. We argue,
theoretically and experimentally, that this leads to higher clustering
accuracy. We also present a fast algorithm for computing these distances.
