Mining Algorithm for Weighted FP-Growth Frequent Item Sets based on Ordered FP-Tree

  • Yuanyuan Li
  • Shaohong Yin
Keywords: Data Mining, Association Rules, Ordered FP-Tree, Weighted Model, Weighted Ordered FP-Tree


FP-growth algorithm is a classic algorithm of mining frequent item sets, but there exist certain disadvantages for mining the weighted frequent item sets. Based on the weighted downward closure property of the weighted model, this paper proposed a method to reduce the use of storage space by constructing a weight ordered FP-tree, so as to improve the generation efficiency of weighted frequent item sets.


Download data is not yet available.


Agrawal, Rakesh & Srikant, et al. (1994). Fast algorithms for mining association rules. In: International Conference on Very Large Data Bases. Santiago, Chile, pp. 487-499.

Han J, Pei J, & Yin Y. (2000). Mining frequent patterns without candidate generation. In: ACM SIGMOD International Conference on Management of Data. Dallas, TX, United states, pp. 1-12.

Wen Chen. (2012). Mining algorithm for weighted frequent pattern based on Fp tree. Computer Engineering, 38(6), 63-65.

C.H. Cai, W.C. Ada, W.C. Fu, & C.H. Cheng, et al. (1998). Mining association rules with weighted items. In: Proceedings of the International Database Engineering and Application Symposium. Cardiff, UK, pp. 68-77.

F. Tao, F. Murtagh, & M. Farid. (2003). Weighted association rule mining using weighted support and significance framework. In: 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03. Washington, DC, United states, pp. 661-666.

Zhaopeng Pan, Peiyu Liu, & Jing Yi. (2018). An improved fp-tree algorithm for mining maximal frequent patterns. In: 10th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA). Changsha, China, pp. 309-312.

T. Saha, et al. (2018). Association rules to analyze hospital resources with mortality rates. In: 5th International Conference on Business and Industrial Research (ICBIR). Bangkok, Thailand, pp. 51-56.

Yan Shi & Yan Fu. (2006). Algorithm for frequent pattern mining based on fp reference tree/list. Computer Science, 33(6), 206-209.

K. Sun & F. Bai. (2008). Mining weighted association rules without preassigned weights. IEEE Transactions on Knowledge and Data Engineering, 20(4), 489-495.

Yan Wang, Haiyan Xue, & Lingling Li, et al. (2010). An improved algorithm for mining weighted frequent itemsets. Computer Engineering and Applications, 46(23), 135-137+197.

Haitao Hao & Yuanyuan Ma. (2016). Research on ecommerce commodity recommendation system-based on mining algorithm of weighted association rules. Modern Electronic Technology, 39(15), 133-136.

Shuai Yue & Shaohong Yin. (2018). Study on frequent patterns mining based on sorted FP-Tree and two-dimensional table. Journal of Harbin University of Commerce(Natural Science Edition), 34(6), 692-697.

Hongguang Xiao, Guoqun Deng, & Wen Tan, et al. (2018). A weighted association rules mining algorithm based on matrix compression. Measurement and Control Technology, 37(3), 10-13.

How to Cite
Yuanyuan Li, & Shaohong Yin. (2019). Mining Algorithm for Weighted FP-Growth Frequent Item Sets based on Ordered FP-Tree. International Journal of Engineering and Management Research, 9(5), 154-158.