CAJ | 학술논문

针对现有频繁闭项目集挖掘算法存在的不足，提出了一种基于粒度计算的频繁闭项目集挖掘算法。通过混合进制数的变化来生成候选项目集，避免使用了复杂的数据结构，减少了内存和CPU的开销；利用粒度计算的分而治之思想来计算频繁闭项目集的支持度，避免了多次重复扫描数据库，减少了计算复杂度和I/O开销。实验结果表明该算法比经典的频繁闭项目集挖掘算法快速而有效。
침대현유빈번폐항목집알굴산법존재적불족，제출료일충기우립도계산적빈번폐항목집알굴산법。통과혼합진제수적변화래생성후선항목집，피면사용료복잡적수거결구，감소료내존화CPU적개소；이용립도계산적분이치지사상래계산빈번폐항목집적지지도，피면료다차중복소묘수거고，감소료계산복잡도화I/O개소。실험결과표명해산법비경전적빈번폐항목집알굴산법쾌속이유효。
Aiming to these shortcomings from the present frequent closed itemsets mining algorithms, this paper proposes an algorithm of frequent closed itemsets mining based on granular computing. The algorithm uses the varying mixed radix number to generate candidate itemsets, and avoids adopting the complex data structure to reduce the memory and the CPU overhead. And it uses divide and rule for granular computing to compute the support of frequent closed itemsets, and avoids reading repeatedly the database to reduce the computation complexity and I/O overhead. These experimental results indicate that the algorithm is faster and more efficient than these classical mining algorithms for frequent closed itemsets.