Dimensionality reduction is a commonly used step in many algorithms for visualization, classification, clustering and modeling. Most dimensionality reduction algorithms find a low...
Background: High-throughput peptide and protein identification technologies have benefited tremendously from strategies based on tandem mass spectrometry (MS/MS) in combination wi...
Nico Pfeifer, Andreas Leinenbach, Christian G. Hub...
Abstract. This paper presents a method that uses gene ontologies, together with the paradigm of relational subgroup discovery, to help find description of groups of genes different...
Feature weighting or selection is a crucial process to identify an important subset of features from a data set. Removing irrelevant or redundant features can improve the generali...
Clustering of large data bases is an important research area with a large variety of applications in the data base context. Missing in most of the research efforts are means for g...
Alexander Hinneburg, Daniel A. Keim, Markus Wawryn...