The diameter k-clustering problem is the problem of partitioning a finite subset of Rd into k subsets called clusters such that the maximum diameter of the clusters is minimized. ...
Clustering is a common methodology for analyzing the gene expression data. In this paper, we present a new clustering algorithm from an information-theoretic point of view. First,...
"Cluster analysis is an important technique in the rapidly growing field known as exploratory data analysis and is being applied in a variety of engineering and scientific dis...
We present a unification-based, context-sensitive escape and effect analysis that infers lightweight method summaries describing heap effects. The analysis is parameterized on two...
Social networks are ubiquitous. The discovery of close-knit clusters in these networks is of fundamental and practical interest. Existing clustering criteria are limited in that c...
Nina Mishra, Robert Schreiber, Isabelle Stanton, R...