Abstract—Fuzzy association rules use fuzzy logic to convert numerical attributes to fuzzy attribute. In this paper, we present an efficient algorithm named fuzzy cluster-based (FCB) along with its parallel version named parallel fuzzy cluster-based (PFCB). The FCB method is to create cluster tables by scanning the database once, and then clustering the transaction records to the i-th cluster table,where the length of a record is i. moreover, the fuzzy large itemsets are generated by contrasts with the partial cluster tables. Similarly, the PFCB method is to create cluster tables by scanning the database once, and then clustering the transaction records to the i-th cluster table,which is on the i-th processor,where the length of a record is i. moreover, the large itemsets are generated by contrasts with the partial cluster tables. Then, to calculate the fuzzy support of the candidate itemsets at each level, each processor calculates the support of the candidate itemsets in its own cluster and forwards the result to the coordinator. The final fuzzy support of the candidate itemsets, is then calculated from this results in the coordinator. We have performed extensive experiments and compared the performance of our algorithms with two of the best existing algorithms.
Index Terms—Fuzzy Association Rules; cluster table; parallel; itemset;
Amir Ebrahimzadeh is with the Sama technical and vocational training college, Islamic Azad University, Mashhad branch, Mashhad, Iran, (e-mail: ebrahimzadehamir@gmail.com).
Reza Sheibani is with the Department of Computer, Mashhad Branch, Islamic Azad University, Mashhad, Iran. (e-mail: reza.shni@gmail.com).
Cite: Amir Ebrahimzadeh and Reza Sheibani, "Two Efficient Algorithms for Mining Fuzzy Association Rules," International Journal of Machine Learning and Computing vol. 1, no. 5, pp. 510-515, 2011.