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6 April 2000 Fuzzification of attribute-oriented generalization and its application to medicine
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Conventional studies on knowledge discovery in databases (KDD) shows that combination of rule induction methods and attribute-oriented generalization is very useful to extract knowledge from data. However, attribute-oriented generalization in which concept hierarchy is used for transformation of attributes assumes that a given hierarchy is consistent. Thus, if this condition is violated, application of hierarchical knowledge generates inconsistent rules. In this paper, first, we show that this phenomenon is easily found in data mining contexts: when we apply attribute- oriented generalization to attributes in databases, generalized attributes will have fuzziness for classification. Then, we introduce two approaches to solve this problem, one process of which suggests that combination of rule induction and attribute-oriented generalization can be used to validate concept hierarchy. Finally, we briefly discuss the mathematical generalization of this solution in which context- free fuzzy sets is a key idea.
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Shusaku Tsumoto "Fuzzification of attribute-oriented generalization and its application to medicine", Proc. SPIE 4057, Data Mining and Knowledge Discovery: Theory, Tools, and Technology II, (6 April 2000);

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