Data Mining of Multi-Categorized Data
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.
Cite
Text
Abe et al. "Data Mining of Multi-Categorized Data." European Conference on Machine Learning, 2007. doi:10.1007/978-3-540-68416-9_15Markdown
[Abe et al. "Data Mining of Multi-Categorized Data." European Conference on Machine Learning, 2007.](https://mlanthology.org/ecmlpkdd/2007/abe2007ecml-data/) doi:10.1007/978-3-540-68416-9_15BibTeX
@inproceedings{abe2007ecml-data,
title = {{Data Mining of Multi-Categorized Data}},
author = {Abe, Akinori and Hagita, Norihiro and Furutani, Michiko and Furutani, Yoshiyuki and Matsuoka, Rumiko},
booktitle = {European Conference on Machine Learning},
year = {2007},
pages = {182-195},
doi = {10.1007/978-3-540-68416-9_15},
url = {https://mlanthology.org/ecmlpkdd/2007/abe2007ecml-data/}
}