Improving Efficiency of Incremental Mining by Trie Structure and Pre-Large Itemsets


  • Thien-Phuong Le Pacific Ocean University, Nha Trang
  • Bay Vo Division of Data Science, Ton Duc Thang University, Ho Chi Minh City & Faculty of Information Technology, Ton Duc Thang University, Ho Chi Minh City
  • Tzung-Pei Hong Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan
  • Bac Le Department of Computer Science, University of Science, VNU-HCM
  • Dosam Hwang Department of Computer Engineering, Yeungnam University


Data mining, frequent itemset, incremental mining, pre-large itemset, trie


Incremental data mining has been discussed widely in recent years, as it has many practical applications, and various incremental mining algorithms have been proposed. Hong et al. proposed an efficient incremental mining algorithm for handling newly inserted transactions by using the concept of pre-large itemsets. The algorithm aimed to reduce the need to rescan the original database and also cut maintenance costs. Recently, Lin et al. proposed the Pre-FUFP algorithm to handle new transactions more efficiently, and make it easier to update the FP-tree. However, frequent itemsets must be mined from the FP-growth algorithm. In this paper, we propose a Pre-FUT algorithm (Fast-Update algorithm using the Trie data structure and the concept of pre-large itemsets), which not only builds and updates the trie structure when new transactions are inserted, but also mines all the frequent itemsets easily from the tree. Experimental results show the good performance of the proposed algorithm.


Download data is not yet available.




How to Cite

Le, T.-P., Vo, B., Hong, T.-P., Le, B., & Hwang, D. (2015). Improving Efficiency of Incremental Mining by Trie Structure and Pre-Large Itemsets. COMPUTING AND INFORMATICS, 33(3), 609–632. Retrieved from



Special Section Articles