Open Access
ARTICLE
An Improved Algorithm for Mining Correlation Item Pairs
Tao Li1, Yongzhen Ren1, *, Yongjun Ren2, Jinyue Xia3
1 College of Electronic and Information Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
2 College of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, 210044, China.
3 International Business Machines Corporation (IBM), New York, USA.
* Corresponding Author: Yongzhen Ren. Email: .
Computers, Materials & Continua 2020, 65(1), 337-354. https://doi.org/10.32604/cmc.2020.06462
Received 26 February 2019; Accepted 13 June 2019; Issue published 23 July 2020
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.
Keywords
Cite This Article
T. Li, Y. Ren, Y. Ren and J. Xia, "An improved algorithm for mining correlation item pairs,"
Computers, Materials & Continua, vol. 65, no.1, pp. 337–354, 2020.