Abstract
Decision Tree Induction (DTI), one of the Data Mining classification methods, is used in this research for predictive problem solving in analyzing patient medical track records. In this paper, we extend the concept of DTI dealing with meaningful fuzzy labels in order to express human knowledge for mining fuzzy association rules. Meaningful fuzzy labels (using fuzzy sets) can be defined for each domain data. For example, fuzzy labels poor disease, moderate disease, and severe disease are defined to describe a condition/type of disease. We extend and propose a concept of fuzzy information gain to employ the highest information gain for splitting a node. In the process of generating fuzzy association rules, we propose some fuzzy measures to calculate their support, confidence and correlation. The designed application gives a significant contribution to assist decision maker for analyzing and anticipating disease epidemic in a certain area.
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Intan, R., Yuliana, O.Y. (2009). Fuzzy Decision Tree Induction Approach for Mining Fuzzy Association Rules. In: Leung, C.S., Lee, M., Chan, J.H. (eds) Neural Information Processing. ICONIP 2009. Lecture Notes in Computer Science, vol 5864. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-10684-2_80
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DOI: https://doi.org/10.1007/978-3-642-10684-2_80
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