TY - JFULL AU - Elsayeda M. Elgaml and Dina M. Ibrahim and Elsayed A. Sallam PY - 2015/6/ TI - Improved FP-growth Algorithm with Multiple Minimum Supports Using Maximum Constraints T2 - International Journal of Computer and Information Engineering SP - 1121 EP - 1129 VL - 9 SN - 1307-6892 UR - https://publications.waset.org/pdf/10001311 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 101, 2015 N2 - 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 “MSFP-growth” 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: Apriori-multiple minimum supports using maximum constraints and FP-growth. 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.  ER -