Advertisement

Sliding-Window Based Method to Discover High Utility Patterns from Data Streams

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 33)

Abstract

High utility pattern mining is one of the emerging researches in data mining. Mining these patterns from the evolving data streams is a big challenge, due the characteristics of data streams like high arrival rate, unbounded and gigantic in size, etc. Commonly there are three window models (landmark window, sliding window, time fading window) used in data streams. However, in most applications, users are interested in recent happenings. Hence, sliding window model has attracted high interest among three. Many approaches have been proposed based on the sliding window model. However, most of the approaches are based on level-wise candidate generation and text approach. In view of this, we propose an efficient one pass, tree based approach for mining high utility patterns over data streams. Experimental results show that the performance of our approach is better than the level-wise approach.

Keywords

Data streams Sliding window High utility pattern Frequent pattern mining Data mining 

References

  1. 1.
    Shen, Y.D., Zhang, Z., Yang, Q.: Objective-oriented utility-based association mining. In: Proceedings of 2002 IEEE International Conference on Data Mining, 2002, IEEE. ICDM 2002, pp. 426–433 (2002)Google Scholar
  2. 2.
    Yao, H., Hamilton, H.J., Butz, C.J.: A foundational approach to mining itemset utilities from databases. In: The 4th SIAM International Conference on Data Mining, pp. 482–486 (2004)Google Scholar
  3. 3.
    Yao, H., Hamilton, H.J.: Mining itemset utilities from transaction databases. Data Knowl. Eng. 59(3), 603–626 (2006)CrossRefGoogle Scholar
  4. 4.
    Agrawal, R., Srikant, R., et al.: Fast algorithms for mining association rules. In: Proceedings of 20th International Conference on Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994) Google Scholar
  5. 5.
    Liu, Y., Liao, W.K., Choudhary, A.: A two-phase algorithm for fast discovery of high utility itemsets. In: Ho, T., Cheung, D., Liu, H. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 689–695. Springer, New York (2005)Google Scholar
  6. 6.
    Liu, Y., Liao, W.K., Choudhary, A.: A fast high utility itemsets mining algorithm. In: Proceedings of the 1st International Workshop on Utility-Based Data Mining, ACM, pp. 90–99 (2005)Google Scholar
  7. 7.
    Jiang, N., Gruenwald, L.: Research issues in data stream association rule mining. ACM Sigmod Rec. 35(1), 14–19 (2006)CrossRefGoogle Scholar
  8. 8.
    Cheung, D.W., Han, J., Ng, V.T., Wong, C.: Maintenance of discovered association rules in large databases: an incremental updating technique. In: Proceedings of the Twelfth International Conference on Data Engineering, IEEE, 1996, pp. 106–114 (1996)Google Scholar
  9. 9.
    Deypir, M., Sadreddini, M.H., Hashemi, S.: Towards a variable size sliding window model for frequent itemset mining over data streams. Comput. Ind. Eng. 63(1), 161–172 (2012)CrossRefGoogle Scholar
  10. 10.
    Chi, Y., Wang, H., Yu, P.S., Muntz, R.R.: Moment: maintaining closed frequent itemsets over a stream sliding window. In: Fourth IEEE International Conference on Data Mining, 2004, IEEE. ICDM’04, pp. 59–66 (2004)Google Scholar
  11. 11.
    Golab, L., Ozsu, M.T.: Issues in data stream management. ACM Sigmod Rec. 32(2), 5–14 (2003)CrossRefGoogle Scholar
  12. 12.
    Gaber, M.M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: a review. ACM Sigmod Rec 34(2), 18–26 (2005)CrossRefGoogle Scholar
  13. 13.
    Lee, C.H., Lin, C.R., Chen, M.S.: Sliding-window filtering: an efficient algorithm for incremental mining. In: Proceedings of the Tenth International Conference on Information and Knowledge Management, ACM, pp. 263–270 (2001)Google Scholar
  14. 14.
    Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: Third IEEE International Conference on Data Mining, 2003, IEEE. ICDM 2003, pp. 19–26 (2003)Google Scholar
  15. 15.
    Chu, C.J., Tseng, V.S., Liang, T.: An efficient algorithm for mining temporal high utility itemsets from data streams. J. Syst. Softw. 81(7), 1105–1117 (2008)CrossRefGoogle Scholar
  16. 16.
    Li, H.F., Huang, H.Y., Chen, Y.C., Liu, Y.J., Lee, S.Y.: Fast and memory efficient mining of high utility itemsets in data streams. In: Eighth IEEE International Conference on Data Mining, 2008, IEEE. ICDM’08, pp. 881–886 (2008)Google Scholar

Copyright information

© Springer India 2015

Authors and Affiliations

  1. 1.Indian School of MinesDhanbadIndia

Personalised recommendations