Abstract
Comprehensibility is vital in results of medical data mining systems since doctors simply require it. Another important issue specific to some data sets, like Fitness, is their uniform distribution due to tile analysis that was performed on them. In this paper, we propose a novel data mining tool named OSDM (Optimized Shape Distribution Method) to give a comprehensive view of correlations of attributes in cases of uneven frequency distribution among different values of symptoms. We apply OSDM to explore the relationship of the Fitness data and symptoms in medical test dataset for which popular data mining methods fail to give an appropriate output to help doctors decisions. In our experiment, OSDM found several useful relationships.
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© 2006 Springer-Verlag Berlin Heidelberg
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Sami, A., Nagatomi, R., Takahashi, M., Tokuyama, T. (2006). OSDM: Optimized Shape Distribution Method. In: Li, X., Zaïane, O.R., Li, Z. (eds) Advanced Data Mining and Applications. ADMA 2006. Lecture Notes in Computer Science(), vol 4093. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11811305_115
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DOI: https://doi.org/10.1007/11811305_115
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37025-3
Online ISBN: 978-3-540-37026-0
eBook Packages: Computer ScienceComputer Science (R0)