Elsayeda M. Elgaml and Dina M. Ibrahim and Elsayed A. Sallam
Improved FPgrowth Algorithm with Multiple Minimum Supports Using Maximum Constraints
1122 - 1129
2015
9
5
International Journal of Computer and Information Engineering
https://publications.waset.org/pdf/10001311
https://publications.waset.org/vol/101
World Academy of Science, Engineering and Technology
Association rule mining is one of the most important fields of data mining and knowledge discovery. In this paper, we propose an efficient multiple support frequent pattern growth algorithm which we called “MSFPgrowth” that enhancing the FPgrowth algorithm by making infrequent child node pruning step with multiple minimum support using maximum constrains. The algorithm is implemented, and it is compared with other common algorithms Apriorimultiple minimum supports using maximum constraints and FPgrowth. The experimental results show that the rule mining from the proposed algorithm are interesting and our algorithm achieved better performance than other algorithms without scarifying the accuracy.
Open Science Index 101, 2015