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Diagnosis of Melanoma Using IRIM, a Data Mining System

  • Jerzy W. Grzymala-Busse
  • Jay Hamilton
  • Zdzislaw S. Hippe
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3070)

Abstract

Melanoma is a very dangerous skin cancer. In this paper we present results of experiments on three melanoma data sets. Two data mining tools were used, a new system called IRIM (Interesting Rule Induction Module) and well established LEM2 (Learning from Examples Module, version 2), both are components of the same data mining system LERS (Learning from Examples based on Rough Sets). Typically IRIM induces the strongest rules that are possible for a data set. IRIM does not need any preliminary discretization or preprocessing of missing attribute values. Though performance of IRIM and LEM2 is fully comparable, IRIM provides an additional opportunity to induce unexpected and strong rules supplying an important insight helpful for diagnosis of melanoma.

Keywords

Numerical Attribute Rule Induction Strong Rule Data Mining Tool Symbolic Attribute 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Jerzy W. Grzymala-Busse
    • 1
    • 2
  • Jay Hamilton
    • 1
  • Zdzislaw S. Hippe
    • 3
  1. 1.Department of Electrical Engineering and Computer ScienceUniversity of KansasLawrenceUSA
  2. 2.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  3. 3.Department of Expert Systems and Artificial IntelligenceUniversity of Information Technology and ManagementRzeszowPoland

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