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Partitioning Algorithms and Combined Model Integration for Data Mining

Author

Listed:
  • Claudio Conversano

    (Università di Napoli Federico II)

  • Francesco Mola

    (Università di Cagliari)

  • Roberta Siciliano

    (Università di Napoli Federico II)

Abstract

Summary In this paper a data-driven procedure is introduced enabling to extract information from complex and huge data sets for statistical purposes. The proposed strategy consists of three stages: tree-partitioning, modelling and model fusion. As a result, we define a final complex decision rule for supervised classification and prediction. Main tools are represented by the tree production rules and nonlinear regression models from the class of Generalized Additive Multi-Mixture Models. The benchmark of the proposed strategy is shown using a well-known real data set.

Suggested Citation

  • Claudio Conversano & Francesco Mola & Roberta Siciliano, 2001. "Partitioning Algorithms and Combined Model Integration for Data Mining," Computational Statistics, Springer, vol. 16(3), pages 323-339, September.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:3:d:10.1007_s001800100070
    DOI: 10.1007/s001800100070
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    References listed on IDEAS

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    1. Siciliano, Roberta & Mola, Francesco, 2000. "Multivariate data analysis and modeling through classification and regression trees," Computational Statistics & Data Analysis, Elsevier, vol. 32(3-4), pages 285-301, January.
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    Cited by:

    1. Tomàs Aluja-Banet & Eduard Nafria, 2003. "Stability and scalability in decision trees," Computational Statistics, Springer, vol. 18(3), pages 505-520, September.

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