Novel Operations for FP-Tree Data Structure and Their Applications

  • Tri-Thanh Nguyen
  • Quang-Thuy Ha
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 282)


Frequent Pattern Tree (FP-tree) proposed by Han et al. is a data structure that is used for storing frequent patterns (or itemsets) in association rule mining. FP-tree helps to reduce the number of database (DB) scans to only two, and shrink down the number of candidates of frequent patterns. This paper proposes to define some operations on the FP-tree in order to empower its application. With the devised operations, we can: a) incrementally build the FP-tree when only a subset of a DB is ready at a time; b) construct FP-tree in parallel with low cost of communication; c) build local FP-trees independently based on local database and then use them to construct the global FP-tree in a distributed system; d) prune the FP-tree according to different values of minimum support threshold for frequent pattern mining.


Association rules data structure frequent pattern mining FP-tree parallel processing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining Association Rules between Sets of Items in Large Databases. In: Proc. of SIGMOD Conference 1993, pp. 207–216 (1993)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. Research Report RJ 9839, IBM Almaden Research Center (1994)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules in Large Databases. In: VLDB 1994, pp. 487–499 (1994)Google Scholar
  4. 4.
    Agrawal, R., Aggarwal, C., Prasad, V.V.V.: Depth-first generation of large item-sets for association rules. IBM Tech. Report RC21538 (July 1999)Google Scholar
  5. 5.
    Bernecker, T., Kriegel, H.-P., Renz, M., Verhein, F., Züfle, A.: Probabilistic Frequent Pattern Growth for Itemset Mining in Uncertain Databases (Technical Report). CoRR abs/1008.2300 (2010)Google Scholar
  6. 6.
    Brin, S., Motwani, R., Silverstein, C.: Beyond Market Baskets: Generalizing Association Rules to Correlations. In: Proc. of SIGMOD Conference 1997, pp. 265–276 (1997)Google Scholar
  7. 7.
    Han, J., Pei, J., Yin, Y.: Mining Frequent Patterns without Candidate Generation. In: Proc. of SIGMOD Conference 2000, pp. 1–12 (2000)Google Scholar
  8. 8.
    Kumar, B.S., Rukmani, K.V.: Implementation of Web Usage Mining Using APRIORI and FP-Growth Algorithms. Int. J. of Advanced Networking and Applications 1(6), 400–404 (2010)Google Scholar
  9. 9.
    Li, H., Wang, Y., Zhang, D., Zhang, M., Chang, E.Y.: Pfp: parallel fp-growth for query recommendation. In: Proc. of RecSys 2008, pp. 107–114 (2008)Google Scholar
  10. 10.
    Lin, W.-Y., Huang, K.-W., Wu, C.-A.: MCFPTree: An FP-tree-based algo-rithm for multi-constraint patterns discovery. IJBIDM 5(3), 231–246 (2010)CrossRefMathSciNetGoogle Scholar
  11. 11.
    Lin, C.-Y., Chen, Y.-A., Tseng, Y.-C., Wang, L.-C.: A flexible analysis and prediction framework on resource usage in public clouds. In: Proc. of CloudCom 2012, pp. 309–316 (2012)Google Scholar
  12. 12.
    Patro, S.N., Mishra, S., Khuntia, P., Bhagabati, C.: Construction of FP Tree using Huffman Coding. International Journal of Computer Science Issues 9(3), 446–469 (2012)Google Scholar
  13. 13.
    Shrivastava, V.K., Kumar, P., Pardasani, K.R.: FP-tree and COFI Based Approach for Mining of Multiple Level Association Rules in Large Data-bases. CoRR abs/1003.1821 (2010)Google Scholar
  14. 14.
    Silverstein, C., Brin, S., Motwani, R., Ullman, J.D.: Scalable Techniques for Mining Causal Structures. In: Proc. of VLDB 1998, pp. 594–605 (1998)Google Scholar
  15. 15.
    Singh, M., Ahirwar, R., Kher, N.: FP-Tree Improve Efficiency & Increase Scalability by Applying Parallel Projected. Binary Journal of Data Mining & Networking 1(1), 14–16 (2010)Google Scholar
  16. 16.
    Suman, M., Anuradha, T., Gowtham, K., Ramakrishna, A.: A Frequent Pattern Mining Algorithm Based On Fp-Tree Structure Andapriori Algorithm. International Journal of Engineering Research and Applications 2(1), 114–116 (2012)Google Scholar
  17. 17.
    Wang, J., Han, J., Lu, Y., Tzvetkov, P.: TFP: An Efficient Algorithm for Mining Top-K Frequent Closed Itemsets. IEEE Trans. Knowl. Data Eng. 17(5), 652–664 (2005)CrossRefGoogle Scholar
  18. 18.
    Xu, A.-P., Tang, Y., Wang, Q., Qiao, M.-Q., Zhang, H.-Y., Wei, S.: Mining Associated Factors about Emotional Disease Bases on FP-Tree Growing Algorithm. International Journal of Engineering and Manufacturing 4, 25–31 (2011)CrossRefGoogle Scholar
  19. 19.
    Yen, S.-J., Wang, C.-K., Ouyang, L.-Y.: A Search Space Reduced Algorithm for Mining Frequent Patterns. J. Inf. Sci. Eng. 28(1), 177–191 (2012)Google Scholar
  20. 20.
    Zaki, M.J., Parthasarathy, S., Ogihara, M., Li, W.: New Algorithms for Fast Discovery of Association Rules. In: Proc. of KDD 1997, pp. 283–286 (1997)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Tri-Thanh Nguyen
    • 1
    • 2
  • Quang-Thuy Ha
    • 1
    • 2
  1. 1.Vietnam National University(VNU)HanoiVietnam
  2. 2.University of Engineering and Technology (UET)LahorePakistan

Personalised recommendations