We revisit and use the dependence transformation method to generate parallel algorithms suitable for cluster and grid computing. We illustrate this method in two applications: to o...
Ulisses Kendi Hayashida, Kunio Okuda, Jairo Panett...
We propose an efficient sequential Monte Carlo inference scheme for the recently proposed coalescent clustering model [1]. Our algorithm has a quadratic runtime while those in [1]...
Our goal is to develop a robust out-of-core sorting program for a distributed-memory cluster. The literature contains two dominant paradigms for out-of-core sorting algorithms: me...
We study Hamming versions of two classical clustering problems. The Hamming radius p-clustering problem (HRC) for a set S of k binary strings, each of length n, is to find p bina...
Maximum margin clustering (MMC) is a recently proposed clustering method, which extends the theory of support vector machine to the unsupervised scenario and aims at finding the m...