FP-Growth Algorithm

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An FP-Growth Algorithm is a complete frequent pattern set finding algorithm that uses an FP-tree (an extended prefix-tree structure for storing compressed information about frequent patterns).

References

2014

  • https://en.wikibooks.org/wiki/Data_Mining_Algorithms_In_R/Frequent_Pattern_Mining/The_FP-Growth_Algorithm
    • QUOTE: In Data Mining the task of finding frequent pattern in large databases is very important and has been studied in large scale in the past few years. Unfortunately, this task is computationally expensive, especially when a large number of patterns exist.

      The FP-Growth Algorithm, proposed by Han in [1], is an efficient and scalable method for mining the complete set of frequent patterns by pattern fragment growth, using an extended prefix-tree structure for storing compressed and crucial information about frequent patterns named frequent-pattern tree (FP-tree). In his study, Han proved that his method outperforms other popular methods for mining frequent patterns, e.g. the Apriori Algorithm [2] and the TreeProjection [3]. In some later works [4] it was proved that FP-Growth has better performance than other methods, including Eclat [5] and Relim [6]. The popularity and efficiency of FP-Growth Algorithm contributes with many studies that propose variations to improve his performance [7] [8] [9] [10] [11] [12] [13] [14] [15] [16].

      This chapter describes the algorithm and some variations and discuss features of the R language and strategies to implement the algorithm to be used in the R. Next a briefly conclusion and future works are proposed.

  1. ↑ J. Han, H. Pei, and Y. Yin. Mining Frequent Patterns without Candidate Generation. In: Proc. Conf. on the Management of Data (SIGMOD’00, Dallas, TX). ACM Press, New York, NY, USA 2000.
  2. ↑ Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proc. 1994 Int. Conf. Very Large Data Bases (VLDB’94), Santiago, Chile, pp. 487–499.
  3. ↑ Agarwal, R., Aggarwal, C., and Prasad, V.V.V. 2001. A tree projection algorithm for generation of frequent itemsets. Journal of Parallel and Distributed Computing, 61:350–371.
  4. ↑ B.Santhosh Kumar and K.V.Rukmani. Implementation of Web Usage Mining Using APRIORI and FP Growth Algorithms. Int. J. of Advanced Networking and Applications, Volume: 01, Issue:06, Pages: 400-404 (2010).
  5. ↑ M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li. New Algorithms for Fast Discovery of Association Rules. Proc. 3rd Int. Conf. on Knowledge Discovery and Data Mining (KDD’97), 283–296. AAAI Press, Menlo Park, CA, USA 1997.
  6. ↑ Christian Borgelt. Keeping Things Simple: Finding Frequent Item Sets by Recursive Elimination. Workshop Open Source Data Mining Software (OSDM'05, Chicago, IL), 66-70. ACM Press, New York, NY, USA 2005
  7. ↑ Cornelia Gyorödi and Robert Gyorödi. A Comparative Study of Association Rules Mining Algorithms.
  8. ↑ F. Bonchi and B. Goethals. FP-Bonsai: the Art of Growing and Pruning Small FP-trees. Proc. 8th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’04, Sydney, Australia), 155–160. Springer-Verlag, Heidelberg, Germany 2004.
  9. ↑ Aiman Moyaid, Said and P.D.D., Dominic and Azween, Abdullah. A Comparative Study of FP-growth Variations. international journal of computer science and network security, 9 (5). pp. 266-272.
  10. ↑ Liu,G. , Lu ,H. , Yu ,J. X., Wang, W., & Xiao, X.. AFOPT: An Efficient Implementation of Pattern Growth Approach, In Proc. IEEE ICDM'03 Workshop FIMI'03, 2003.
  11. ↑ Grahne, G. , & Zhu, J. Fast Algorithm for frequent Itemset Mining Using FP-Trees. IEEE Transactions on Knowledge and Data Engineer,Vol.17,NO.10, 2005.
  12. ↑ Gao, J. Realization of new Association Rule Mining Algorithm. Int. Conf. on Computational Intelligence and Security ,IEEE, 2007.
  13. ↑ Cornelia Gyorödi, Robert Gyorödi, T. Cofeey & S. Holban. Mining association rules using Dynamic FP-trees. in Proceedings of The Irish Signal and Systems Conference, University of Limerick, Limerick, Ireland, 30th June 2nd July 2003, ISBN 0-9542973-1-8, pag. 76-82.
  14. ↑ F. Bonchi, F. Giannotti, A. Mazzanti, and D. Pedreschi. Exante: Anticipated data reduction in constrained pattern mining. In Proc. of PKDD03.
  15. ↑ Balázes Rácz. Nonordfp: An FP-Growth Variation without Rebuilding the FP-Tree. 2nd Int'l Workshop on Frequent Itemset Mining Implementations FIMI2004.
  16. ↑ Grahne O. and Zhu J. Efficiently Using Prefix-trees in Mining Frequent Itemsets, In Proc. of the IEEE ICDM Workshop on Frequent Itemset Mining, 2004.
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