The users often face difficulties in interpreting and exploiting the association rules extracted from large transaction data with high dimensionality.There are two main reasons.Firstly,too many association rules can be produced by the conventional association rule mining algorithms,and secondly,some association rules can be partly overlapped.This problem can be solved if the users can select the relevant items to be used in association rule mining.In this context,this paper aims to propose a new visual exploration tool,structured association map,which enables the users to find the group of the relevant items in a visual way.For illustration,this procedure is applied to a mass health examination result data set,and the experiment results demonstrate that structured association map with maximum sums of 2 × 2 regular contributions value helps to reduce the complexities of association analysis significantly and it enables to focus on the specific region of the search space of association rule mining while avoiding the irrelevant association rules.%对于大量的高维度的交易数据,利用关联规则进行数据挖掘,用户难以进行解释和利用.主要两个原因:常规关联规则挖掘算法可产生大量关联规则;一些关联规则可部分重叠.若用户能自主选择,在关联规则挖掘中所使用的相关项集,则可解决该问题.提出一种新的视觉探索工具,结构化关联映射图,使用户能够以视觉方式找到相关项集的组.该方法使用健康检查结果数据集进行验证,并且实验结果表明具有最高2×2规则贡献的和值的结构化关联映射图有助于显著减少关联分析的复杂性,并且能够集中于搜索空间的特定区域关联规则挖掘,同时避免不相关的关联规则.
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