CAJ | 학술논문

讨论了在多值属性关系中进行关联规则挖掘的应用特点,提出利用数据整理和数值编码的方式对关联规则挖掘算法进行优化.将目标数据属性按其在算法中的作用划分,并分别进行转换和编码;然后对数据先进行聚类,再在聚类结果中发掘频繁项目集;最后利用聚类后关联规则快速更新算法获取关联规则.算法分析和实验结果表明,该算法比传统的关联规则挖掘算法更有效率.
토론료재다치속성관계중진행관련규칙알굴적응용특점,제출이용수거정리화수치편마적방식대관련규칙알굴산법진행우화.장목표수거속성안기재산법중적작용화분,병분별진행전환화편마;연후대수거선진행취류,재재취류결과중발굴빈번항목집;최후이용취류후관련규칙쾌속경신산법획취관련규칙.산법분석화실험결과표명,해산법비전통적관련규칙알굴산법경유효솔.
This paper argued the characters of mining association rules in relation of quantitative attribute, and presented a method by coordinating data and coding numerical value to improve the algorithm of mining association rules. At first, partitioned the target data by the function in algorithm, and converted and ceded. The second, clustered the data, and then discovered frequent items sets in the result of clustering. At last, discovered association rules by the algorithm of mining association rules after clustering. Through the experiment and the analysis of algorithm, it is improves that the algorithm is more efficiently than conventional ones.