Abstract
At the International Research and Educational Institute for Integrated Medical Sciences (IREIIMS) project, we are collecting complete medical data sets to determine relationships between medical data and health status. Since the data include many items which will be categorized differently, it is not easy to generate useful rule sets. Sometimes rare rule combinations are ignored and thus we cannot determine the health status correctly. In this paper, we analyze the features of such complex data, point out the merit of categorized data mining and propose categorized rule generation and health status determination by using combined rule sets.
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Abe, A., Hagita, N., Furutani, M., Furutani, Y., Matsuoka, R. (2008). Data Mining of Multi-categorized Data. In: Raś, Z.W., Tsumoto, S., Zighed, D. (eds) Mining Complex Data. MCD 2007. Lecture Notes in Computer Science(), vol 4944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68416-9_15
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DOI: https://doi.org/10.1007/978-3-540-68416-9_15
Publisher Name: Springer, Berlin, Heidelberg
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