A clustering method based on rough sets and its application to knowledge discovery in the medical database.

This paper proposes a clustering method for nominal and numerical data based on Rough Sets and its application to knowledge discovery in the medical database. Classification is performed according to the indiscernibility relations defined on the basis of relative similarity between objects. The similarity is defined as a combination of two types of similarity measures: the Hamming distance for nominal attributes and the Mahalanobis distance for numerical attributes. Excessive generation of small category is suppressed by modifying similar equivalence relations into the same equivalence relation. An analysis of the meningoencephalitis diagnosis database was performed to validate this method. The result showed that this method could deal well with both types of attributes and discover the primary factors for diagnosis.