title: Outlier Identification in Model-Based Cluster Analysis.
abstract: In model-based clustering based on normal-mixture models, a few outlying observations can influence the cluster structure and number. This paper develops a method to identify these, however it does not attempt to identify clusters amidst a large field of noisy observations. We identify outliers as those observations in a cluster with minimal membership proportion or for which the cluster-specific variance with and without the observation is very different. Results from a simulation study demonstrate the ability of our method to detect true outliers without falsely identifying many non-outliers and improved performance over other approaches, under most scenarios. We use the contributed R package MCLUST for model-based clustering, but propose a modified prior for the cluster-specific variance which avoids degeneracies in estimation procedures. We also compare results from our outlier method to published results on National Hockey League data.
authors.affiliation: Dupont, DuET Applied Statistics, Delaware USA.
authors.forename: Katie
authors.initials: K
authors.lastname: Evans
authors.affiliation: University of Rochester, Rochester USA.
authors.forename: Tanzy
authors.initials: T
authors.lastname: Love
authors.affiliation: University of Rochester, Rochester USA.
authors.forename: Sally W
authors.initials: SW
authors.lastname: Thurston
keywords: Influential points; MCLUST; National Hockey League; Normal-mixture models; Prior