WRSP-Miner Algorithm for Mining Weighted Sequential Patterns from Spatio-temporal Databases

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 379)


Not allowing priorities in the mining process does not support user-directed or focus-driven mining. The work proposed in this paper provides support to include user prioritizations in the form of weights into the mining process. An algorithm WRSP-Miner is proposed for the purpose of mining Weighted Regional Sequential Patterns (WRSPs) from spatio-temporal event databases. WRSP-Miner uses two interestingness measures sequence weight and significance index for efficient mining of WRSPs. Experimentation has been performed on synthetic datasets and results proved that the proposed WRSP-Miner algorithm has achieved the purpose of its design.


Data mining Spatiotemporal database Event Sequential pattern Weighted patterns 


  1. 1.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Proceedings of 1995 International Conference on Data Engineering (1995)Google Scholar
  2. 2.
    Tsoukatos, I., Gunopulos, D.: Efficient mining of spatiotemporal patterns, pp. 425–442. Springer, Berlin, Heidelberg, (2001)Google Scholar
  3. 3.
    Wang, J., Hsu, W., Lee, M.L.: Flow miner: finding flow patterns in spatio-temporal databases. In: 16th IEEE International Conference on Tools with Artificial Intelligence, pp. 14–21 (2004)Google Scholar
  4. 4.
    Wang, J., Hsu, W., Lee, M.L.: Mining generalized spatio-temporal patterns. In: Database Systems for Advanced Applications, pp. 649–661. Springer, Berlin, Heidelberg (2005)Google Scholar
  5. 5.
    Huang, Y., Zhang, L., Zhang, P.: A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20(4), 433–448 (2008)CrossRefGoogle Scholar
  6. 6.
    Salas, H.A., Bringay, S., Flouvat, F., Selmaoui-Folcher, N., Teisseire, M.: The pattern next door: towards spatio-sequential pattern discovery. In: Advances in Knowledge Discovery and Data Mining, pp. 157–168. Springer, Berlin, Heidelberg (2012)Google Scholar
  7. 7.
    Fabrègue, M., Braud, A., Bringay, S., Le Ber, F., Teisseire, M.: Including spatial relations and scales within sequential pattern extraction, pp. 209–223. Discovery Science, Springer, Berlin, Heidelberg (2012)Google Scholar
  8. 8.
    Cai, C.H., Chee Fu, A.W., Cheng, C.H., Kwong, W.W.: Mining association rules with weighted items. In: Proceedings of the Sixth International Conference on Intelligent Data Engineering and Automated Learning (1998)Google Scholar
  9. 9.
    Wang, W., Yang, J., Yu, P.S.: Efficient mining of weighted association rules (WAR). In: Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 270–274 (2000)Google Scholar
  10. 10.
    Tao, F.: Weighted association rule mining using weighted support and significant framework. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 661–666 (2003)Google Scholar
  11. 11.
    Yun, U., Leggett, J.J.: WFIM: Weighted frequent itemset mining with a weight range and a minimum weight. In: Proceedings of the Fourth SIAM International Conference on Data Mining, pp. 636—640 (2005)Google Scholar
  12. 12.
    Yun, U., Leggett, J.J.: WLPMiner: weighted frequent pattern mining with length decreasing support Constraints. In: Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 555–567 (2005)Google Scholar
  13. 13.
    Yun, U., Leggett, J.J.: WIP: mining weighted interesting patterns with a strong weight and/or support affinity. SDM 6, 3477–3499 (2006)Google Scholar
  14. 14.
    Yun, U., Legget, J.J.: WSpan: weighted sequential pattern mining in large sequence databases. In: 3rd International IEEE Conference on Intelligent Systems, pp. 512–517, IEEE (2006)Google Scholar
  15. 15.
    Yun, U.: WIS: Weighted interesting sequential pattern mining with a similar level of support and/or weight. ETRI J. 29(3), 336–352 (2007)CrossRefGoogle Scholar
  16. 16.
    Chang, J.H.: Mining weighted sequential patterns in a sequence database with a time-interval weight. Knowl. Based Syst. 24(1), 1–9 (2011)CrossRefGoogle Scholar
  17. 17.
    Sunitha, G., Rama Mohan Reddy, A.: A region-based framework for mining sequential patterns from spatio-temporal event databases. Int. J. Appl. Eng. Res. 9(24), 28161–28175 (2014)Google Scholar

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© Springer India 2016

Authors and Affiliations

  1. 1.Department of CSES. V. University College of Engineering, S. V. UniversityTirupatiIndia

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