Document clustering has long been an important problem in information retrieval. In this paper, we present a new clustering algorithm ASI1, which uses explicitly modeling of the s...
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...
This paper introduces a new visual representation of a document or group of documents, a Dynamic Document Icon, or Dydocon. Its representation is symbolic like an icon, but change...
Clustering data in high dimensions is believed to be a hard problem in general. A number of efficient clustering algorithms developed in recent years address this problem by proje...
Kamalika Chaudhuri, Sham M. Kakade, Karen Livescu,...
We develop a novel on-line built-in self-test (BIST) technique for testing FPGAs that has a very high diagnosability even in presence of clustered faults, a fault pattern for whic...