We present an EM-based clustering method that can be used for constructing or augmenting ontologies such as MeSH. Our algorithm simultaneously clusters verbs and nouns using both ...
Vasileios Kandylas, Lyle H. Ungar, Ted Sandler, Sh...
We propose a scalable technique called Seeded Clustering that allows us to maintain R-tree indices by bulk insertion while keeping pace with high data arrival rates. Our approach ...
We argue that when objects are characterized by many attributes, clustering them on the basis of a random subset of these attributes can capture information on the unobserved attr...
We consider the use of a database cluster for Application Service Provider (ASP). In the ASP context, applications and databases can be update-intensive and must remain autonomous....
Background: Hierarchical clustering is a widely applied tool in the analysis of microarray gene expression data. The assessment of cluster stability is a major challenge in cluste...