Incrementally Mining Frequent Patterns from Large Database

  • Yue-Shi Lee
  • Show-Jane YenEmail author
Part of the Studies in Big Data book series (SBD, volume 8)


Mining frequent patterns is an important task in data mining area, which is to find the itemsets frequently purchased together from a transaction database. However, the transactions will grow rapidly, such that the size of the transaction database becomes bigger and bigger due to the addition of the new transactions. The users may eager for getting the latest frequent patterns from the large database as soon as possible in order to make the best decision. Therefore, it has become an important issue to propose an efficient method for finding the latest frequent patterns when the transactions keep being added into the database. Although tree-based approaches have been recently adopted in most of the studies in this field, they have to re-scan the original database and generate a large tree structure. In this paper, we propose two efficient algorithms which only keep frequent items in a condensed tree structure. When a set of new transactions is added into the database, our algorithms can efficiently update the tree structure without scanning the original database.


Data mining Frequent pattern Incremental mining Tree structure Transaction database 


  1. 1.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of the 20th International Conference on Very Large Data Bases (VLDB), pp. 487–499 (1994)Google Scholar
  2. 2.
    Mohammad, E.H., Osmar, R.Z.: COFI-tree mining: a new approach to pattern growth with reduced candidacy generation. In: Workshop on Frequent Itemset Mining Implementations (FIMI’03) in conjunction with IEEE-ICDMGoogle Scholar
  3. 3.
    Jiawei Han, Jian Pei and Yiwen Yin, “Mining Frequent Patterns without Candidate Generation. In: Proceedings of the 2000 ACM International Conference on Management of Data (SIGMOD), pp. 1–12 (2000)Google Scholar
  4. 4.
    Lee, C.-F., Shen, T.-H.: An FP-split method for fast association rules mining. In: Proceedings of the 3rd International Conference on Information Technology: Research and Education (ITRE), pp. 459–463 (2005)Google Scholar
  5. 5.
    Wang, K., Tang, L., Han, J., Liu, J.: Top down FP-growth for association rule mining. In: Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining, pp. 334–340 (2002)Google Scholar
  6. 6.
    Yen S.J., Lee, Y.S., Wang C.K., Wu C.W.: The Studies of Mining Frequent Patterns Based on Frequent Pattern Tree. PAKDD 2009, LNAI 5476, pp. 232–241 (2009)Google Scholar
  7. 7.
    Cheung, D.W., Han, J., Ng, V.T., Wong, C.Y.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the 12th International Conference on Data Engineering (ICDE), pp. 106–114 (1996)Google Scholar
  8. 8.
    Cheung, D.W., Lee, S.D., Kao, B.: A general incremental technique for maintaining discovered association rules. In: Proceedings of the 5th International Conference on Database Systems for Advanced Applications (DASFAA), pp. 185–194 (1997)Google Scholar
  9. 9.
    Cheung, W., Zaïane, O.R.: Incremental mining of frequent patterns without candidate generation or support constraint. In: Proceedings of the 7th International Database Engineering and Applications Symposium (IDEAS), pp. 111–116 (2003)Google Scholar
  10. 10.
    Hong, T.-P., Lin, C.-W., Yu-Lung, W.: Incrementally fast updated frequent pattern trees. Expert Syst. Appl. Int. J. 34(4), 2424–2435 (2008)CrossRefGoogle Scholar
  11. 11.
    Koh, J.-L., Shieh, S.-F.: An efficient approach for maintaining association rules based on adjusting FP-tree structures. In: Proceedings of the 12th International Conference on Database Systems for Advanced Applications (DASFAA), pp. 417–424 (2004)Google Scholar
  12. 12.
    Leung, C.K.-S., Khan, Q.I., Hoque, T.: CanTree: a tree structure for efficient incremental mining of frequent patterns. In: Proceedings of the 5th IEEE International Conference on Data Mining (ICDM), pp. 274–281 (2005)Google Scholar
  13. 13.

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and Information EngineeringMing Chuan UniversityTaoyuanTaiwan

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