Missing association rule mining algorithm using tensor decomposition in cloud computing environment

For the issue that the relative error of association rules discovering is high caused by underlying data missing on the cloud computing environment,a distributed discovering algorithm of missing association rule based on tensor decomposition is proposed to model association rules,missing data and approximate their confidences. Firstly,Apriori algorithm is used to locally data related so as to obtaining frequent item sets. Then,CANDECOMP / PARAFAC( CP) decomposition method is used to decompose tensor confidence,iterate is done by using conjugate gradient algorithm to minimize the cost of the approximate tensor. Finally,local correlation and global correlation is combined to discover missing association rules by distributed algorithm in the case of missing data. The simulation results show that the average relative error of the proposed algorithm is only 5. 55%,the maximum error is less than 10%,which is less than several advanced missing association rule algorithms. The proposed algorithm has reduced average execution time with 16. 5% compared with clustering-based association rule algorithm,which indicates that the proposed algorithm has better performance than other missing association rules and it can provide the approximate solution of missing rules confidence.