Partitioning Algorithms and Combined Model Integration for Data Mining

Claudio Conversano, Francesco Mola and Roberta Siciliano
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Claudio Conversano: Università di Napoli Federico II
Francesco Mola: Università di Cagliari
Roberta Siciliano: Università di Napoli Federico II

Computational Statistics, 2001, vol. 16, issue 3, No 2, 323-339

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.

Keywords: Exploratory Trees; Generalized Additive Models; Mixing Parameters; Bootstrap Averaging; Backfitting Algorithm; Generalized Additive Multi-Mixture Models; Resampling; Model Fusion (search for similar items in EconPapers)
Date: 2001
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s001800100070

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