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.
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Grzymala-Busse, J.W., Hamilton, J., Hippe, Z.S. (2004). Diagnosis of Melanoma Using IRIM, a Data Mining System. In: Rutkowski, L., Siekmann, J.H., Tadeusiewicz, R., Zadeh, L.A. (eds) Artificial Intelligence and Soft Computing - ICAISC 2004. ICAISC 2004. Lecture Notes in Computer Science(), vol 3070. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24844-6_155
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DOI: https://doi.org/10.1007/978-3-540-24844-6_155
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