CAJ | 학술논문

挖掘频繁项集是数据挖掘中最基本的问题之一，而大型数据库庞大的数据使得传统的频繁模式挖掘算法难以适用。针对大型数据库的特点，在分析FP-growth算法的基础上，提出一种基于等价类的大型数据库频繁模式挖掘算法EFP-growth（Equivalent Classes Frequent Patterns-Growth）算法。EFP-growth算法利用项集等价类将关联规则挖掘的项集分成互不相交的子空间的性质，将一个大型数据库分解成多个投影数据库，依次在每一个投影数据库上进行约束频繁项集挖掘。算法尤其适合支持度较小时的大型数据库的挖掘。分析和实验表明EFP-growth算法在挖掘大型数据库时时间和空间的性能上均优于FP-growth算法。而且，随着数据库规模的增大，EFP-growth算法具有更明显的优势。
알굴빈번항집시수거알굴중최기본적문제지일，이대형수거고방대적수거사득전통적빈번모식알굴산법난이괄용。침대대형수거고적특점，재분석FP-growth산법적기출상，제출일충기우등개류적대형수거고빈번모식알굴산법EFP-growth（Equivalent Classes Frequent Patterns-Growth）산법。EFP-growth산법이용항집등개류장관련규칙알굴적항집분성호불상교적자공간적성질，장일개대형수거고분해성다개투영수거고，의차재매일개투영수거고상진행약속빈번항집알굴。산법우기괄합지지도교소시적대형수거고적알굴。분석화실험표명EFP-growth산법재알굴대형수거고시시간화공간적성능상균우우FP-growth산법。이차，수착수거고규모적증대，EFP-growth산법구유경명현적우세。
Finding frequent itemsets is one of the most basic problems in data mining. The large amounts of data make the traditional algorithms for frequent patterns mining difficult to extend to large databases. According to characteristic of large databases, inspired by the fact that the FP-growth provides an effective algorithm, a new EFP-growth for mining frequent patterns in large databases is proposed. Based on the characteristic of equivalent classes , which separate item sets of association rules into many subsets , proposed algorithm divides a large database into many projection subsets and carries out constrained frequent. Experiments show that the algorithm has accelerated the mining speed and the performance of space scalability is superior to the FP-growth algorithm. Moreover, the algorithm has a very good time and space scalability with the increasing size of database.