Alan P. Sprague

TL;DR: The authors present a novel process and system—the Data Mining Surveillance System (DMSS)—that utilize association rules to identify new and interesting patterns in surveillance data and indicate potentially significant shifts in the occurrence of infection or antimicrobial resistance patterns of P. aeruginosa.

TL;DR: A linear time algorithm for unit interval graph recognition based on Breadth-First Search, which produces an ordering of the vertices of the graph whenever G is a unit intervals graph.

TL;DR: A mature version of DMSS is described as well as an experiment in which DMSS was used to analyze all inpatient culture data, collected over 15 months at the University of Alabama at Birmingham Hospital.

TL;DR: The findings suggest several plausible scenarios: (1) microarray datasets lack natural clustering structure thereby producing low stability scores on all four methods; (2) the algorithms studied do not produce reliable results and/or (3) sample sizes typically used in microarray research may be too small to support derivation of reliable clustering results.

TL;DR: This paper demonstrates that SEQOPTICS performs better based on some evaluation criteria including Jaccard coefficient, Precision, and Recall and is a promising protein sequence clustering method with future possible improvement on parallel computing and other protein distance measurements.