CAJ | 학술논문

为了解决传统关联规则挖掘中候选集数量过多,计算时间复杂度过高的问题,提出了基于语义相关性的关联规则挖掘方法.该方法采用本体概念之间的语义相关性描述领域中的复杂关系,通过语义相关度过滤掉领域中相关性较小的候选集,以减少关联规则挖掘中候选集的数量.计算语义相关性时,将本体层次关系看作有向无环图而不是层次树,不仅考虑直接层次关系,还考虑非直接层次关系和其他典型语义关系.实验结果表明,该方法能有效减少候选集数量,提高关联规则挖掘的效率.
위료해결전통관련규칙알굴중후선집수량과다,계산시간복잡도과고적문제,제출료기우어의상관성적관련규칙알굴방법.해방법채용본체개념지간적어의상관성묘술영역중적복잡관계,통과어의상관도과려도영역중상관성교소적후선집,이감소관련규칙알굴중후선집적수량.계산어의상관성시,장본체층차관계간작유향무배도이불시층차수,불부고필직접층차관계,환고필비직접층차관계화기타전형어의관계.실험결과표명,해방법능유효감소후선집수량,제고관련규칙알굴적효솔.
An association rules mining method based on semantic relativity is proposed to solve the problem that there are more candidate item sets and higher time complexity in traditional association rules mining. Semantic relativity of ontology concepts is used to describe complicated relationships of domains in the method. Candidate item sets with less semantic relativity are filtered to reduce the number of candidate item sets in association rules mining. An ontology hierarchy relationship is regarded as a directed acyclic graph rather than a hierarchy tree in the semantic relativity computation. Not only direct hierarchy relationships, but also non-direct hierarchy relationships and other typical semantic relationships are taken into account. Experimental results show that the proposed method can reduce the number of candidate item sets effectively and improve the efficiency of association rules mining.