CAJ | 학술논문

在关联规则挖掘领域有很多算法，其中最经典的是Apriori算法，该算法可找出所有的频繁项集，并发现项目间的关联关系，但是执行效率却很低。针对经典Apriori算法中存在的I/O过重，产生频繁项集，计算量过大等问题，提出了一种Apriori的改进方案I_Apriori，通过减少扫描数据库次数，降低候选项集计算复杂度以及减少预剪枝步骤计算量等途径提高了算法的执行效率。对比分析了Apriori和I_Apriori算法，I_Apriori算法计算复杂度更低，同时进行了对比实验，结果表明相比于Apriori算法，I_Apriori算法执行效率更高。
재관련규칙알굴영역유흔다산법，기중최경전적시Apriori산법，해산법가조출소유적빈번항집，병발현항목간적관련관계，단시집행효솔각흔저。침대경전Apriori산법중존재적I/O과중，산생빈번항집，계산량과대등문제，제출료일충Apriori적개진방안I_Apriori，통과감소소묘수거고차수，강저후선항집계산복잡도이급감소예전지보취계산량등도경제고료산법적집행효솔。대비분석료Apriori화I_Apriori산법，I_Apriori산법계산복잡도경저，동시진행료대비실험，결과표명상비우Apriori산법，I_Apriori산법집행효솔경고。
There are many algorithms in the field of association rule mining, the most classic one is Apriori algorithm, which is used to find frequent itemsets and discovery association rules between projects, however, the execution effi-ciency of this algorithm is very low. Aiming at the problems of excessive I/O burden and large amount of calculation in the process of producing frequent itemsets and so on, this paper presents an improved scheme based on Apriori algorithm, named I_Apriori, which improves the efficiency of the algorithm execution by reducing the times of scanning database, reducing the computational complexity of candidate itemsets and reducing the calculation amount of the pre-pruning step and other ways. Contrasting Apriori and I_Apriori, the computational complexity of I_Apriori is lower. By contrast test, the results show that I_Apriori algorithm performs efficiently compared to the Apriori algorithm.