Mining Fuzzy Association Rules on Has-A and Is-A Hierarchical Structures
Been-Chian Chien*, Ming-Huang Zhong**, and Jeng-Jung Wang**
*Department of Computer Science and Information Engineering, National University of Tainan, 33, Sec. 2, Su-Lin Street, Tainan 70005, Taiwan, R.O.C.
**Department of Information Engineering, I-Shou University Kaohsiung, 1, Section 1, Hsueh-Cheng Rd., Ta-Hsu Hsiang, Kaohsiung County, Taiwan 840, R.O.C.
Preliminary studies on data mining focus on finding association rules from transaction databases containing items without relationships among them. However, relationships among items often exist in real applications. Most of the previous works only concern about Is-A hierarchy. In this paper, hierarchical relationships include a Has-A hierarchy and multiple Is-A hierarchies are discussed. The proposed method first reduces a Has-A & Is-A hierarchy into an extended Has-A hierarchy using the IsA-Reduce algorithm. The quantitative data is transformed into fuzzy items. The RPFApriori algorithm is then applied to find fuzzy association rules from the fuzzy item data and the extended Has-A hierarchy.
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