Time-Fading Based High Utility Pattern Mining from Uncertain Data Streams

Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)

Abstract

Recently, high utility pattern mining from data streams has become a great challenge to the data mining researchers due to rapid changes in technology. Data streams are continuous flow of data with rapid rate and huge volumes. There are mainly three widow models namely: Landmark window, sliding window and time-fading window used over the data streams in different applications. In many applications knowledge discovery from the data which is available in current window is required to respond quickly. Next the Landmark window keeps the information from the specific time point to the present time. Where as in time-fading model information is also captured from the landmark time to current time but it assigns the different weights to the different batches or transactions. Time-fading model is mainly suitable for mining the uncertain data which is generated by many sources like sensor data streams and so on. In this paper, we have proposed an approach using time-fading window model to mine high utility patterns from uncertain data streams.

Keywords

Data streams time-fading window uncertain data high utility patterns 

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References

  1. 1.
    Gaber, M., Zaslavsky, A., Krishnaswamy, S.: Mining data streams: a review. ACM Sigmod Record 34(2), 18–26 (2005)CrossRefGoogle Scholar
  2. 2.
    Aggarwal, C.: Data streams: models and algorithms, vol. 31. Springer (2007)Google Scholar
  3. 3.
    Yao, H., Hamilton, H., Butz, C.: A foundational approach to mining itemset utilities from databases. In: The 4th SIAM International Conference on Data Mining, pp. 482–486 (2004)Google Scholar
  4. 4.
    Shie, B.E., Yu, P., Tseng, V.: Efficient algorithms for mining maximal high utility itemsets from data streams with different models. Expert Systems with Applications 39(17), 12947–12960 (2012)CrossRefGoogle Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. 20th Int. Conf. Very Large Data Bases, VLDB, vol. 1215, pp. 487–499 (1994)Google Scholar
  6. 6.
    Ahmed, C., Tanbeer, S., Jeong, B.S.: Efficient mining of high utility patterns over data streams with a sliding window method, pp. 99–113 (2010)Google Scholar
  7. 7.
    Ahmed, C., Tanbeer, S., Jeong, B.S., Choi, H.J.: Interactive mining of high utility patterns over data streams. Expert Systems with Applications 39(15), 11979–11991 (2012)CrossRefGoogle Scholar
  8. 8.
    Leung, C.S., Jiang, F.: Frequent itemset mining of uncertain data streams using the damped window model. In: Proceedings of the 2011 ACM Symposium on Applied Computing, pp. 950–955. ACM (2011)Google Scholar
  9. 9.
    Aggarwal, C., Yu, P.: A survey of uncertain data algorithms and applications. IEEE Transactions on Knowledge and Data Engineering 21(5), 609–623 (2009)CrossRefGoogle Scholar
  10. 10.
    Chan, R., Yang, Q., Shen, Y.D.: Mining high utility itemsets. In: Third IEEE International Conference on Data Mining, ICDM 2003, pp. 19–26. IEEE (2003)Google Scholar
  11. 11.
    Liu, Y., Liao, W.K., Choudhary, A.: A two-phase algorithm for fast discovery of high utility itemsets, pp. 689–695 (2005)Google Scholar
  12. 12.
    Giannella, C., Han, J., Pei, J., Yan, X., Yu, P.: Mining frequent patterns in data streams at multiple time granularities. Next Generation Data Mining 212, 191–212 (2003)Google Scholar
  13. 13.
    Tseng, V., Chu, C.J., Liang, T.: Efficient mining of temporal high utility itemsets from data streams. In: Second International Workshop on Utility-Based Data Mining, p. 18. Citeseer (2006)Google Scholar
  14. 14.
    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 (ICDM), pp. 881–886. IEEE (2008)Google Scholar
  15. 15.
    Leung, C.K.-S., Jiang, F.: Frequent pattern mining from time-fading streams of uncertain data. In: Cuzzocrea, A., Dayal, U. (eds.) DaWaK 2011. LNCS, vol. 6862, pp. 252–264. Springer, Heidelberg (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Computer Science & EngineeringIndian School of MinesDhanbadIndia

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