本文针对传统关联规则挖掘算法产生大量冗余规则,提出了对关联规则结果进行二次挖掘,并设计了算法对挖掘出的关联规则进行聚类,然后基于已有领域知识对聚类后的关联规则进行新颖度评价,对于新颖度较高价值较大的关联规则可以存储于领域知识库用于决策使用或再次挖掘过程。该算法有效的减少的规则的数量,提高了规则的新颖性和精确度,对商业应用具有很高的价值。文章最后使用UCI开源数据进行了实验分析,并验证了该算法的有效性。
Second mining of the result of association rule mining is proposed in solution of the large numbers of redundant rules in the traditional association rules mining algorithm, and the algorithm for clustering of association rules is designed, then the novelty of the association rules is assessed after clustering based on the existing domain knowledge. It is insited that the association rules with more novelty and higher value can be stored in the domain knowledge base, and can be used for the decision or mining again. The algorithm proposed in this paper is effective to reduce the number of rules and also help to improve the novelty and precision of rule, which has a very high value for business applications. Finally the open source data from UCI is used to carry on the experiment to verify the effectiveness of the algorithm.
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