We consider the problem of clustering in its most basic form where only a local metric on the data space is given. No parametric statistical model is assumed, and the number of cl...
Abstract. The interpretation of large-scale protein network data depends on our ability to identify significant sub-structures in the data, a computationally intensive task. Here w...
Jacob Scott, Trey Ideker, Richard M. Karp, Roded S...
We present a new data structure that facilitates approximate nearest neighbor searches on a dynamic set of points in a metric space that has a bounded doubling dimension. Our data...
A flow is said to be confluent if at any node all the flow leaves along a single edge. Given a directed graph G with k sinks and non-negative demands on all the nodes of G, we con...
Many feature detection algorithms rely on the choice of scale. In this paper, we complement standard scaleselection algorithms with spatial regularization. To this end, we formula...