A methodology for automatically identifying and clustering semantic features or topics in a heterogeneous text collection is presented. Textual data is encoded using a low rank no...
Farial Shahnaz, Michael W. Berry, V. Paul Pauca, R...
This paper describes a theoretical framework for inducing knowledge from incomplete data sets. The general framework can be used with any formalism based on a lattice structure. It...
Data clustering is a popular approach for automatically finding classes, concepts, or groups of patterns. In practice this discovery process should avoid redundancies with existi...
It is considered the minimum sum-of-squares clustering problem. The mathematical modeling of this problem leads to a min − sum − min formulation which, in addition to its intr...
We propose an algorithm that predicts potentially missing Gene Ontology annotations, in order to speed up the time-consuming annotation curation process. The proposed method extend...
Marco Tagliasacchi, Roberto Sarati, Marco Masserol...