@Article{cmc.2020.06462,
AUTHOR = {Tao Li, Yongzhen Ren, Yongjun Ren, Jinyue Xia},
TITLE = {An Improved Algorithm for Mining Correlation Item Pairs},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {337--354},
URL = {http://www.techscience.com/cmc/v65n1/39569},
ISSN = {1546-2226},
ABSTRACT = {Apriori algorithm is often used in traditional association rules mining, searching 
for the mode of higher frequency. Then the correlation rules are obtained by detected the 
correlation of the item sets, but this tends to ignore low-support high-correlation of 
association rules. In view of the above problems, some scholars put forward the positive 
correlation coefficient based on Phi correlation to avoid the embarrassment caused by 
Apriori algorithm. It can dig item sets with low-support but high-correlation. Although the 
algorithm has pruned the search space, it is not obvious that the performance of the running 
time based on the big data set is reduced, and the correlation pairs can be meaningless. This 
paper presents an improved mining algorithm with new association rules based on 
interestingness for correlation pairs, using an upper bound on interestingness of the 
supersets to prune the search space. It greatly reduces the running time, and filters the 
meaningless correlation pairs according to the constraints of the redundancy. Compared 
with the algorithm based on the Phi correlation coefficient, the new algorithm has been 
significantly improved in reducing the running time, the result has pruned the redundant 
correlation pairs. So it improves the mining efficiency and accuracy.},
DOI = {10.32604/cmc.2020.06462}
}