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Algorithm of Non-Redundant Association Rules based on User Interest Orientation

Volume 14, Number 8, August 2018, pp. 1745-1753
DOI: 10.23940/ijpe.18.08.p11.17451753

Xiaodong Qiana and Rui Guob

aSchool of Economics and Management, Lanzhou Jiaotong University, Lanzhou, 730070, China
bSchool of Electronics and Information Engineering, Lanzhou Jiaotong University, Lanzhou, 730070, China

(Submitted on May 9, 2018; Revised on June 16, 2018; Accepted on July 23, 2018)


Traditional association rule mining algorithms usually generate association rules that satisfy the predefined minimum support and confidence threshold. However, with the rapid expansion of the data scale, the set of association rules obtained from traditional association rules contains massive redundant rules that cannot reflect the specific interests of users. In this paper, we combine the subjective interestingness optimization algorithm with the objective redundant association rule deletion algorithm in an effort to complement each of their shortcomings and improve optimization ability. We also present an algorithm for non-redundant association rules based on user interest orientation. First, an algorithm is proposed to remove redundant association rules based on the first-order predicate formula. Then, the non-redundant association rule set is used as the original data set for classification. By using the attributes or rules of the user's interest as the guiding object, the template idea is added as the carrier of the user's meaning. Based on the user’s prior knowledge, the template is divided into three types: GI, RPC, and PK. Based on this method, the association rules are obtained using the method of interest calculation. The experimental results show that the algorithm has been improved effectively.


References: 8

              1. B. Liu, W. Hsu, S. Chen, and Y. M. Ma, “Analysing the Subjective Interestingness of Association Rules,” Intelligent Systems and Heir Applications, Vol. 15, No. 5, pp. 47-55, 2000
              2. G. Piatetsky-Shapiro and C. J. Matheus, “The Interestingness of Deviations,” U. M. Fayyad, R. Uthurusamy, Knowledge Discovery in Databases. Seattle: AAAI Press, pp. 25-36, 1994
              3. Y. Zhen and Q. X. Zhu, “Improvement and Application of FP-TREE Algorithm based on User Interest,” Computer Engineering and Applications, Vol. 8, No. 11, pp. 143-147, 2012
              4. X. Z. Niu, J. Yang, and M. T. Zhou, “The Optimization Algorithm of Association Rules based on the Subjective Interest,” Journal of Sichuan University: Engineering Science Edition, Vol. 45, No. 4, pp. 131-139, 2013
              5. S. Y. Wei, G. L. Ji, and W. G. Qu, “Redundant Deletion and Clustering of Association Rules,” Journal of Chinese Mini-Micro Computer Systems, No. 27, pp. 111-113, 2006
              6. M. K. Deng and X. C. Shao, “Discrete Mathematics,” Tsinghua University Press, 2014
              7. C. Y. Yuan, “Petri Net Application,” Science Press, 2013
              8. H. Y. Wang, Q. Huang, C. T. Li, and B. Z. Zhu, “Graph Theory Algorithm and its MATLAB Implementation,” Beihang University Press, 2010


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