The activity of post-marketing surveillance results in a collection of large amount of data. The analysis of data is very useful for raising early warnings on possible adverse reactions of drugs. Association rule mining techniques have been heavily explored by the research community for identifying binary association between drugs and their adverse effects. But these techniques perform poorly and miss out several interesting associations when it comes to analysis of multidimensional data which may include multiple patient attributes, drugs and adverse drug reactions. In the present work, a clustering-based hybrid approach has been presented for finding quantitative multidimensional association from the large amount of data. Firstly, it employs clustering technique for segmentation of data into semantically coherent clusters. Furthermore, disproportionality method called proportional reporting ratio is applied on clustered data for generating statistically strong associations. The performance of the proposed methodology has been examined on the data taken from the U.S. Food and Drug Administration Adverse Event Reporting System database corresponding to Aspirin and nine other drugs which are prescribed along with Aspirin. The experimental results show that the proposed approach discovered a number of association rules which are very comprehensive and informative regarding relationship of patient traits and drugs with adverse drug reactions. On comparing experimental results with LPMiner, it is observed that the quantitative association rules discovered by LPMiner are just 8.3% of what have been discovered by the proposed methodology.

售后监视活动导致收集了大量数据。数据分析对于提出有关药物可能不良反应的预警非常有用。研究界已广泛探索关联规则挖掘技术，以识别药物之间的二元关联及其不良影响。但是，这些技术在分析多维数据时表现不佳，并且错过了一些有趣的关联，这些多维数据可能包括多种患者属性，药物和药物不良反应。在当前的工作中，已经提出了一种基于聚类的混合方法，用于从大量数据中找到定量的多维关联。首先，它采用聚类技术将数据分割为语义上一致的聚类。此外，将不成比例的方法（称为比例报告比率）应用于聚类数据以生成统计上强的关联。根据美国食品和药物管理局不良事件报告系统数据库中与阿司匹林和与阿司匹林一起处方的其他九种药物对应的数据，对所提出方法的性能进行了检查。实验结果表明，所提出的方法发现了许多关联规则，这些规则对于患者性状和药物与药物不良反应之间的关系非常全面和有益。通过将实验结果与LPMiner进行比较，可以发现LPMiner发现的定量关联规则仅为所提出方法的8.3％。