Computer Science ›› 2017, Vol. 44 ›› Issue (8): 285-289.doi: 10.11896/j.issn.1002-137X.2017.08.049

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Scaling-up Algorithm of Multi-scale Association Rules

LI Chao, ZHAO Shu-liang, ZHAO Jun-peng, GAO Lin and CHI Yun-xian   

  • Online:2018-11-13 Published:2018-11-13

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

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