Mining Weighted Frequent Patterns Using Adaptive Weights

  • Chowdhury Farhan Ahmed
  • Syed Khairuzzaman Tanbeer
  • Byeong-Soo Jeong
  • Young-Koo Lee
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5326)


Existing weighted frequent pattern (WFP) mining algorithms assume that each item has fixed weight. But in our real world scenarios the weight (price or significance) of an item can vary with time. Reflecting such change of weight of an item is very necessary in several mining applications such as retail market data analysis and web click stream analysis. In this paper, we introduce a novel concept of adaptive weight for each item and propose an algorithm AWFPM (adaptive weighted frequent pattern mining). Our algorithm can handle the situation where the weight (price or significance) of an item may vary with time. Extensive performance analyses show that our algorithm is very efficient and scalable for WFP mining using adaptive weights.


Data mining knowledge discovery weighted frequent pattern mining adaptive weight 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Agrawal, R., Srikant, R.: Fast Algorithms for Mining Association Rules. In: 20th Int. Conf. on Very Large Data Bases (VLDB), pp. 487–499 (1994)Google Scholar
  2. 2.
    Yun, U., Leggett, J.J.: WFIM: weighted frequent itemset mining with a weight range and a minimum weight. In: Fourth SIAM Int. Conf. on Data Mining, USA, pp. 636–640 (2005)Google Scholar
  3. 3.
    Yun, U.: Efficient Mining of weighted interesting patterns with a strong weight and/or support affinity. Information Sciences 177, 3477–3499 (2007)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Zhang, S., Zhang, C., Yan, X.: Post-mining: maintenance of association rules by weighting. Information Systems 28, 691–707 (2003)CrossRefGoogle Scholar
  5. 5.
    Tao, F.: Weighted association rule mining using weighted support and significant framework. In: 9th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, USA, pp. 661–666 (2003)Google Scholar
  6. 6.
    Wang, W., Yang, J., Yu, P.S.: WAR: weighted association rules for item intensities. Knowledge Information and Systems 6, 203–229 (2004)CrossRefGoogle Scholar
  7. 7.
    Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: a frequent-pattern tree approach. Data Mining and Knowledge Discovery 8, 53–87 (2004)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Jiang, N., Gruenwald, L.: Research Issues in Data Stream Association Rule Mining. SIGMOD Record 35(1), 14–19 (2006)CrossRefGoogle Scholar
  9. 9.
    Leung, C.K.-S., Khan, Q.I.: DSTree: A Tree structure for the mining of frequent Sets from Data Streams. In: Perner, P. (ed.) ICDM 2006. LNCS (LNAI), vol. 4065, pp. 928–932. Springer, Heidelberg (2006)Google Scholar
  10. 10.
    Leung, C.K.-S., Khan, Q.I., Li, Z., Hoque, T.: CanTree: a canonical-order tree for incremental frequent-pattern mining. Knowledge and Information Systems 11(3), 287–311 (2007)CrossRefGoogle Scholar
  11. 11.
    Tanbeer, S.K., Ahmed, C.F., Jeong, B.: CP-tree: A tree structure for single pass frequent pattern mining. In: Washio, T., Suzuki, E., Ting, K.M., Inokuchi, A. (eds.) PAKDD 2008. LNCS (LNAI), vol. 5012, pp. 1022–1027. Springer, Heidelberg (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Chowdhury Farhan Ahmed
    • 1
  • Syed Khairuzzaman Tanbeer
    • 1
  • Byeong-Soo Jeong
    • 1
  • Young-Koo Lee
    • 1
  1. 1.Department of Computer EngineeringKyung Hee UniversityKyunggi-doRepublic of Korea

Personalised recommendations