title: Outlier Detection through Bipartite Visual Analytics.
abstract: A critical goal of outlier detection is to determine whether an outlying value was caused by experimental/human error, or by natural biological diversity. However, because univariate or multivariate methods (e.g., box plots and principle component analysis) typically used for outlier detection use unipartite representations, they cannot distinguish whether outliers across a set of variables represent, for example, a single patient or different patients. Here we propose a bipartite visual analytical approach to outlier detection, and demonstrate its usefulness for identifying complex bipartite outliers in a dataset of rickettsioses patients, which enabled domain experts to determine whether the outliers were caused by errors, or by biological diversity.
authors.affiliation: Inst. for Translational Sciences, UTMB;
authors.forename: Suresh K
authors.initials: SK
authors.lastname: Bhavnani
authors.forename: Justin A
authors.initials: JA
authors.lastname: Drake
authors.forename: Bryant
authors.initials: B
authors.lastname: Dang
authors.forename: Shyam
authors.initials: S
authors.lastname: Vishweswaran