An Association Discovery Algorithm Containing Quantitative Attributes with Item Constraints

수량적 속성을 포함하는 항목 제약을 고려한 연관규칙 마이닝 앨고리듬

The problem of discovering association rules has received considerable research attention and several fast algorithms for mining association rules have been developed. In this paper, we propose an efficient algorithm for mining quantitative association rules with item constraints. For categorical attributes, we map the values of the attribute to a set of consecutive integers. For quantitative attributes, we can partition the attribute into values or ranges. While such constraints can be applied as a post-processing step, integrating them into the mining algorithm can reduce the execution time. We consider the problem of integrating constraints that are boolean expressions over the presence or absence of items containing quantitative attributes into the association discovery algorithm using Apriori concept.