Fuzzy transforms method and attribute dependency in data analysis

  • Authors:

  • Ferdinando Di Martino;Vincenzo Loia;Salvatore Sessa

  • Affiliations:

  • Universití degli Studi di Napoli Federico II, Dipartimento di Costruzioni e Metodi Matematici in Architettura, Via Monteoliveto 3, 80134 Napoli, Italy and Universití degli Studi di Saler ...;Universití degli Studi di Salerno, Dipartimento di Matematica e Informatica, Via Ponte Don Melillo, 84084 Fisciano, Italy;Universití degli Studi di Napoli Federico II, Dipartimento di Costruzioni e Metodi Matematici in Architettura, Via Monteoliveto 3, 80134 Napoli, Italy

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2010

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Abstract

In this paper, we present a method based on fuzzy transforms to establish dependencies between numerical attributes in datasets. We find the best fuzzy partitions of the attribute domains with respect to which we determine the direct and inverse fuzzy transforms. We use two specific regression indexes (which must be smaller than a threshold deduced experimentally) for evaluating dependency between numerical attributes. The experiments are conducted on two well known datasets: ''El Nino'' (http://kdd.ics.uci.edu/databases/el_nino/el_nino.data.html) and the remote sensing data determined from US Forest Service (Region 2, Resource Information System, http://kdd.ics.uci.edu/databases/covertype/covertype.data.html). Our results are quite in agreement with the regression analysis of the same data.