Abstract: On the research of quantitative association rules mining in database which contains quantitative attributes, the combination of the quantitative attributes and the intervals associated leads to an unmanageably highly sized itemsets and association rule sets which constitute a hamper toward the efficiency of the mining algorithm. The mutual information entropy of the attributes is studied here, and algorithm BMIQAR which can find the frequent itemsets and association rules from the attributes sets with strong information relationship is designed. The experiments show that due to the prune on the attributes, the research space decreases sharply, so the mining efficiency is improved greatly, and the acquired association rules are high confidence ones.

Key words: data mining, quantitative association rules, mutual information entropy

摘要： 在量化关联规则挖掘中存在量化属性及其取值区间的组合爆炸问题，影响算法效率。提出算法BMIQAR，通过考察量化属性间互信息熵，找到具有强信息关系的属性集，从中得到频繁项集以产生规则。实验表明，由于在属性层进行了剪枝，因此缩减了搜索空间，提高了算法的性能，且能得到绝大多数置信度较高的规则。

关键词: 数据挖掘, 量化关联规则, 互信息熵

CLC Number: 

• TP311