The min-sum k-clustering problem is to partition a metric space (P, d) into k clusters C1, . . . , Ck ⊆ P such that k i=1 p,q∈Ci d(p, q) is minimized. We show the first effi...
Background: Some amino acid residues functionally interact with each other. This interaction will result in an evolutionary co-variation between these residues – coevolution. Ou...
Typically 3-D MR and CT scans have a relatively high resolution in the scanning X;Y plane, but much lower resolution in the axial Z direction. This non-uniform sampling of an obje...
Ken Museth, David E. Breen, Leonid Zhukov, Ross T....
Clustering algorithms typically operate on a feature vector representation of the data and find clusters that are compact with respect to an assumed (dis)similarity measure betwee...
A locality sensitive hashing scheme is a distribution on a family F of hash functions operating on a collection of objects, such that for two objects x, y, PrhF [h(x) = h(y)] = si...