Actionable high-coherent-utility fuzzy itemset mining

作者：Chen Chun Hao^*; Li Ai Fang; Lee Yeong Chyi

来源：Soft Computing, 2014, 18(12): 2413-2424.

DOI：10.1007/s00500-013-1214-4

• Fuzzy set
• Utility fuzzy itemsets
• High-coherent-utility fuzzy itemset
• Membership function
• Domain-driven data mining

Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from quantitative transaction databases. Since each item has its own utility, utility itemset mining has become increasingly important. However, common problems with existing approaches are that an appropriate minimum support is difficult to determine and that the derived rules usually expose common-sense knowledge, which may not be interesting from a business point of view. This study thus proposes an algorithm for mining high-coherent-utility fuzzy itemsets to overcome problems with the properties of propositional logic. Quantitative transactions are first transformed into fuzzy sets. Then, the utility of each fuzzy itemset is calculated according to the given external utility table. If the value is larger than or equal to the minimum utility ratio, the itemset is considered as a high-utility fuzzy itemset. Finally, contingency tables are calculated and used for checking-whether a high-utility fuzzy itemset satisfies four criteria. If so, it is a high-coherent-utility fuzzy itemset. Experiments on the foodmart and simulated datasets are made to show that the derived itemsets by the proposed algorithm not only can reach better profit than selling them separately, but also can provide fewer but more useful utility itemsets for decision-makers.

• 出版日期2014-12