Outlier Identification in Model-Based Cluster Analysis

Katie Evans (), Tanzy Love and Sally Thurston

Journal of Classification, 2015, vol. 32, issue 1, 63-84

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. Copyright Classification Society of North America 2015

Keywords: Normal-mixture models; Influential points; MCLUST; Prior; National Hockey League. (search for similar items in EconPapers)
Date: 2015
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Citations: View citations in EconPapers (3)

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DOI: 10.1007/s00357-015-9171-5

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