Modified FP-Growth: An Efficient Frequent Pattern Mining Approach from FP-Tree

  • Shafiul Alom AhmedEmail author
  • Bhabesh Nath
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11941)


Prefix-tree based FP-growth algorithm is a two step process: construction of frequent pattern tree (FP-tree) and then generates the frequent patterns from the tree. After constructing the FP-tree, if we merely use the conditional FP-trees (CFP-tree) to generate the patterns of frequent items, we may encounter the problem of recursive CFP-tree construction and a huge number of redundant itemset generation. Which also leads to huge search space and massive memory requirement. In this paper, we have proposed a new data structure layout called Modified Conditional FP-tree (MCFP-tree). Moreover, we have proposed a new pattern growth algorithm called Modified FP-Growth (MFP-Growth), which uses both top-down and bottom-up approaches to efficiently generate the frequent patterns without recursively constructing the MCFP-tree. During mining phase only one MCFP-tree is maintained in main memory at any instance and immediately deleted or discarded from the memory after performing the mining. From the experimental analysis, it is noticed that the proposed MFP-Growth algorithm requires less memory to construct the MCFP-tree as compared to conditional FP-tree. Moreover, the execution of the MFP-Growth method is found significantly faster than the traditional FP-Growth as it does not generate redundant patterns.


Association Rule (AR) FP-growth Frequent Pattern (FP) FP-tree Pattern Mining (PM) Data Mining (DM) Frequent Itemset (FI) 


  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Tmining association rules between sets of items in large databases. In: ACM SIGMOD International Conference on Management of Data, vol. 22, pp. 207–216 (1993)CrossRefGoogle Scholar
  2. 2.
    El-Hajj, M., Zaïane, O.R.: Inverted matrix: efficient discovery of frequent items in large datasets in the context of interactive mining. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 109–118. ACM (2003)Google Scholar
  3. 3.
    El-Hajj, M., Zaïane, O.R.: Non-recursive generation of frequent K-itemsets from frequent pattern tree representations. In: Kambayashi, Y., Mohania, M., Wöß, W. (eds.) DaWaK 2003. LNCS, vol. 2737, pp. 371–380. Springer, Heidelberg (2003). Scholar
  4. 4.
    Grahne, G., Zhu, J.: Efficiently using prefix-trees in mining frequent itemsets. In: FIMI, vol. 90 (2003)Google Scholar
  5. 5.
    Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Min. Knowl. Disc. 15(1), 55–86 (2007)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. In: Proceedings of ACMSIGMOD, Dallas, TX, pp. 1–12 (2000)Google Scholar
  7. 7.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Min. Knowl. Disc. 8(1), 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lin, K.C., Liao, I.E., Chen, Z.S.: An improved frequent pattern growth method for mining association rules. Expert Syst. Appl. 38(2011), 5154–5161 (2011)CrossRefGoogle Scholar
  9. 9.
    Liu, J., Pan, Y., Wang, K., Han, J.: Mining frequent item sets by opportunistic projection. In: Proceedings of the Eighth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 229–238. ACM (2002)Google Scholar
  10. 10.
    Pei, J., Han, J., Lu, H., Nishio, S., Tang, S., Yang, D.: H-mine: hyper-structure mining of frequent patterns in large databases. In: Proceedings 2001 IEEE International Conference on Data Mining, pp. 441–448. IEEE (2001)Google Scholar
  11. 11.
    Racz, B.: Nonordfp: an FP-growth variation without rebuilding the FP-tree. In: Proceedings of IEEE ICDM Workshop on Frequent Itemset Mining Implementations (2004)Google Scholar
  12. 12.
    Schlegel, B., Gemulla, R., Lehner, W.: Memory-efficient frequent-itemset mining. In: Proceedings of the 14th International Conference on Extending Database Technology, pp. 461–472. ACM (2011)Google Scholar
  13. 13.
    Sucahyo, Y.G., Gopalan, R.P.: CT-PRO: a bottom-up non recursive frequent itemset mining algorithm using compressed FP-tree data structure. In: FIMI, vol. 4, pp. 212–223 (2004)Google Scholar

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Authors and Affiliations

  1. 1.Tezpur UniversityTezpurIndia

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