OSDM: optimized shape distribution method

Authors:
Ashkan Sami;Ryoichi Nagatomi;Makoto Takahashi;Takeshi Tokuyama
Affiliations:
Graduate School of Engineering, Tohoku University, Japan;Department of Medicine and Science in Sports and Exercise, Graduate School of Medicine, Tohoku University, Japan;Graduate School of Engineering, Tohoku University, Japan;Graduate School of Information Sciences, Tohoku University, Japan
Venue:
ADMA'06 Proceedings of the Second international conference on Advanced Data Mining and Applications
Year:
2006

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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.