Background: Clustering the ESTs from a large dataset representing a single species is a convenient starting point for a number of investigations into gene discovery, genome evolut...
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...
We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectationmaximization (EM) algorithm is used to cluster the traj...
Abstract The segmentation performance of any clustering algorithm is very sensitive to the features in an image, which ultimately restricts their generalization capability. This li...
Mohammed Ameer Ali, Gour C. Karmakar, Laurence S. ...
We present a novel algorithm for agglomerative hierarchical clustering based on evaluating marginal likelihoods of a probabilistic model. This algorithm has several advantages ove...