语义驱动的关联规则挖掘算法研究

摘要

为了使传统的关联规则挖掘算法在结合到具体领域时具有更强的适应性,提出了DS-Apriori算法.该算法建立在语义本体的基础上,根据项集内部的语义相关度动态的确定该项集的最小支持度,并采用了项集语义相关度的增量计算方法.实验结果表明,DS-Apriori算法在很大程度上提高了关联规则挖掘算法的效率和效果.%To make traditional association rule mining algorithms more suitable when applying in concrete fields, DS-Apriori based-on semantic ontology is proposed. DS-Apriori sets the minimal support dynamically according to semantic relativity of the item set. In order to reduce the consumption when calculating the semantic relativity, DS-Apriori proposes an incremental computation method when calculating the semantic relativity. Experimental results show that the accuracy and efficiency is improved greatly when using DS-Apriori.