A Rough Set-Based Clustering Method for Biomedical Informatics
Syoji Kobashi, Takehiko Matsuura, Yutaka Hata, Seturo Imawaki*, and Makoto Ishikawa*
*Ishikawa Hospital

Proc. of Proc. of First Vietnam Japan Symposium on Medical Imaging /
Informatics and Applications
pp. 23-29, Nov. 2001.

This study introduces rough set, which can theoretically represent the roughness of information, into the clustering algorithm. The proposed clustering method evaluates degree of dissimilarly between objects by using the concept of value reduct. The indiscernibility relation, namely clustering result, is obtained by evaluating the degree of dissimilarly with a roughness index. The performance of the algorithm was evaluated by applying it to public databases in UCI Machine Learning Repository, and biomedical informatics data. Both of the clustering results are evaluated by comparing with the conventional methods. The proposed method can also give various clustering results according to the roughness index.