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Cluster Analysis in Practice: Dealing with Outliers in Managerial Research

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  • Humberto Elias Garcia Lopes
  • Marlusa de Sevilha Gosling

Abstract

Context: in recent years, cluster analysis has stimulated researchers to explore new ways to understand data behavior. The computational ease of this method and its ability to generate consistent outputs, even in small datasets, explain that to some extent. However, researchers are often mistaken in holding that clustering is a terrain in which anything goes. The literature shows the opposite: they must be careful, especially regarding the effect of outliers on cluster formation. Objective: in this tutorial paper, we contribute to this discussion by presenting four clustering techniques and their respective advantages and disadvantages in the treatment of outliers. Methods: for that, we worked from a managerial dataset and analyzed it using k-means, PAM, DBSCAN, and FCM techniques. Results: our analyzes indicate that researchers have distinct clustering techniques for dealing with outliers accordingly.Conclusion: we concluded that researchers need to have a more diversified repertoire of clustering techniques. After all, this would give them two relevant empirical alternatives: choose the most appropriate technique for their research objectives or adopt a multi-method approach.

Suggested Citation

  • Humberto Elias Garcia Lopes & Marlusa de Sevilha Gosling, 2021. "Cluster Analysis in Practice: Dealing with Outliers in Managerial Research," RAC - Revista de Administração Contemporânea (Journal of Contemporary Administration), ANPAD - Associação Nacional de Pós-Graduação e Pesquisa em Administração, vol. 25(1), pages 200081-2000.
  • Handle: RePEc:abg:anprac:v:25:y:2021:i:1:1425
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    References listed on IDEAS

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    1. John Adams & Darren Hayunga & Sattar Mansi & David Reeb & Vincenzo Verardi, 2019. "Identifying and treating outliers in finance," Financial Management, Financial Management Association International, vol. 48(2), pages 345-384, June.
    2. J. A. Hartigan & M. A. Wong, 1979. "A K‐Means Clustering Algorithm," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 28(1), pages 100-108, March.
    3. Taweh Beysolow II, 2017. "Introduction to Deep Learning Using R," Springer Books, Springer, number 978-1-4842-2734-3, June.
    4. Nicola Loperfido, 2020. "Kurtosis-based projection pursuit for outlier detection in financial time series," The European Journal of Finance, Taylor & Francis Journals, vol. 26(2-3), pages 142-164, February.
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