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Robust classification with categorical variables

In: Compstat 2006 - Proceedings in Computational Statistics

Author

Listed:
  • Andrea Cerioli

    (Università di Parma, Dipartimento di Economia, Sezione di Statistica e Informatica)

  • Marco Riani

    (Università di Parma, Dipartimento di Economia, Sezione di Statistica e Informatica)

  • Anthony C. Atkinson

    (London School of Economics, Department of Statistics)

Abstract

The forward search provides a powerful and computationally simple approach for the robust analysis of multivariate data. In this paper we suggest a new forward search algorithm for clustering multivariate categorical observations. Classification based on categorical information poses a number of challenging issues that are addressed by our algorithm. These include selection of the number of groups, identification of outliers and stability of the suggested solution. The performance of the algorithm is shown with both simulated and real examples.

Suggested Citation

  • Andrea Cerioli & Marco Riani & Anthony C. Atkinson, 2006. "Robust classification with categorical variables," Springer Books, in: Alfredo Rizzi & Maurizio Vichi (ed.), Compstat 2006 - Proceedings in Computational Statistics, pages 507-519, Springer.
    • Handle: RePEc:spr:sprchp:978-3-7908-1709-6_41
    DOI: 10.1007/978-3-7908-1709-6_41
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