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Outlier Identification in Model-Based Cluster Analysis

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  • Katie Evans
  • Tanzy Love
  • Sally Thurston

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

Suggested Citation

  • Katie Evans & Tanzy Love & Sally Thurston, 2015. "Outlier Identification in Model-Based Cluster Analysis," Journal of Classification, Springer;The Classification Society, vol. 32(1), pages 63-84, April.
    • Handle: RePEc:spr:jclass:v:32:y:2015:i:1:p:63-84
    DOI: 10.1007/s00357-015-9171-5
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    References listed on IDEAS

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    1. Chris Fraley & Adrian E. Raftery, 1999. "MCLUST: Software for Model-Based Cluster Analysis," Journal of Classification, Springer;The Classification Society, vol. 16(2), pages 297-306, July.
    2. Chris Fraley & Adrian E. Raftery, 2007. "Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 155-181, September.
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    Cited by:

    1. Katharine M. Clark & Paul D. McNicholas, 2024. "Finding Outliers in Gaussian Model-based Clustering," Journal of Classification, Springer;The Classification Society, vol. 41(2), pages 313-337, July.
    2. Andrzej Chmielowiec, 2021. "Algorithm for error-free determination of the variance of all contiguous subsequences and fixed-length contiguous subsequences for a sequence of industrial measurement data," Computational Statistics, Springer, vol. 36(4), pages 2813-2840, December.
    3. Douglas L. Steinley, 2016. "Editorial," Journal of Classification, Springer;The Classification Society, vol. 33(3), pages 327-330, October.

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