关键词: 多尺度,关联规则,尺度上推,多尺度关联规则挖掘

Abstract: Great achievements have been made on multi-scale research of data mining.However,multi-scale data mining research is far from being deep and perfect.Current research,which mainly focuses on space and image data,pays less attention to multi-scale data mining on the general data.With the continuous development of big data applications,research of multi-scale data mining becomes particularly important.Regarding the issue above,this paper carried out a study of scale-conversion methods on universal multi-scale association rules data mining.First of all,this paper gave an approach of frequent items based on the similarity theory of including degree.Then,the paper proposed an algorithm named MSARSUA (Multi-Scale Association Rules Scaling Up Algorithm) based on the theory of image pyramid.Finally,experimental results on data sets from H province,UCI and IBM show that algorithm MSARSUA has higher coverage,higher F1-measure and lower estimation error of average support.Algorithm MSARSUA outperforms both Apriori algorithm and FP-Growth algorithm on efficiency aspect.Meanwhile,the results indicate that algorithm MSARSUA possesses superior performance compared with algorithm SU-ARMA.

Key words: Multi-scale,Association rules,Scaling-up,Multi-scale association rules mining