Affiliations: Innovative Information Industry Research Center (IIIRC) | Shenzhen Key Laboratory of Internet Information Collaboration, School of Computer Science and Technology, Harbin, Institute of Technology Shenzhen Graduate School, HIT Campus Shenzhen University Town Xili, Shenzhen, P. R. China | Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, R.O.C. | Department of Computer Science and Engineering, National Sun Yat-sen University, Kaohsiung, Taiwan, R.O.C.
Abstract: In the past, most algorithms proposed for mining association rules handle items with binary values. Transactions with quantitative values are, however, commonly seen in real-world applications. Fuzzy mining algorithms for inducing rules from quantitative databases have thus been developed, most of which are based on the Apriori algorithm. Fuzzy mining is seldom based on frequent pattern (FP) trees because fuzzy-set processing from an FP tree is much more complicated than crisp-set processing. In this paper, a two-phase fuzzy mining approach based on a tree structure to obtain fuzzy frequent itemsets from a quantitative database is proposed. A simple tree structure called the upper-bound fuzzy frequent-pattern (UBFFP) tree is designed. The two-phase fuzzy mining approach can easily derive the upper-bound fuzzy counts of itemsets using the tree. It prunes unpromising itemsets in the first phase, and then finds the actual fuzzy frequent itemsets in the second phase. Experimental results show that the proposed approach has good performance.
