Mining Temporal Association Rules with Incremental Standing for Segment Progressive Filter

  • Mohsin Naqvi
  • Kashif Hussain
  • Sohail Asghar
  • Simon Fong
Part of the Communications in Computer and Information Science book series (CCIS, volume 136)


Association rule mining is a popular data mining technique which dredges up valuable relationships among different items in a dataset. A variant called temporal association rule mining finds relationship between items with respect to particular time periods. Databases are frequently updated; therefore temporal association rules that we discover should be corresponding to the updates in databases. Most of the existing data mining techniques however do not cover revising associate rules from the latest updates in the dataset. Some form of incremental mining technique is also needed to embrace the fresh elements that are updated continuously in the transaction database. In this paper we propose a technique that modifies the frequent patterns in pace with changes to the database over time. An Incremental Standing method for Segment Progressive Filter (ISPF) is proposed. ISPF algorithm is used for supporting the temporal association rule mining in transaction database with different exhibition periods. Our algorithm is optimized such that scanning of database is minimized. Scan reduction technique is applied here to generate all candidate k-item sets to form 2-candidate item sets directly. Working of the proposed algorithm is tested and illustrated with examples and a case study respectively.


Temporal association rules 


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  1. 1.
    Tan, P.-N., Steinbach, M., Kumar, V.: Introduction to Data Mining. Pearson Addison, London (2006)Google Scholar
  2. 2.
    Agrawal, R., lmielinkski, T., Swami, A.: Mining Association Rule Between sets of items in large database. In: Proceeding of ACM SIGMOD, pp. 207–216 (1993)Google Scholar
  3. 3.
    Wang. W., Yang. Y., and Muntz. R.: Temporal Association Rules with Numerical Attributes. NCLA CSD Technical Report 990011 (1999)Google Scholar
  4. 4.
    Gharib, T.F., Nassar, H., Taha, M., Abraham, A.: An efficient algorithm for incremental mining of temporal association rules. Data & Knowledge Engineering, 800–815 (2010)Google Scholar
  5. 5.
    Chen, M., Chen, S.-C., Shyu, M.-L.: Hierarchical Temporal Association Mining for Video Event Detection in Video DatabasesGoogle Scholar
  6. 6.
    Lee, C.-H., Lin, C.-R., Chen, M.-S.: On Mining General Temporal Association Rule in Publication of database. In: ICDM (2002)Google Scholar
  7. 7.
    Ale, J.M., Rossi, G.H.: An Approach to Discovering Temporal Rules. ACM Press, New York (2000)Google Scholar
  8. 8.
    Byon, L.-N., Han, J.-H.: Fast for Temporal Association Rule in a Large Database. Key Engineering Materials, 287–279 (2005)Google Scholar
  9. 9.
    Chang, C.-Y., Chen, M.-S., Lee, C.-H.: Mining General Temporal Association Rule for item with different exhibition period. IEEE, Los Alamitos (2002)CrossRefGoogle Scholar
  10. 10.
    Lee, C.-H., Ou, J.C., Chen, M.-S.: Progressive Weighted Miner: An efficient method for time constraints mining. In: Whang, K.-Y., Jeon, J., Shim, K., Srivastava, J. (eds.) PAKDD 2003. LNCS (LNAI), vol. 2637, Springer, Heidelberg (2003)Google Scholar
  11. 11.
    Pandey, A., Pardasani, K.R.: PPCI algorithm for mining temporal association rules in large database. International Journal of information and knowledge (April 2009)Google Scholar
  12. 12.
    Lin, Y., Ning, P.: Discovering Calendric based temporal association rule. In: Proceeding of the 8th International Symposium on Temporal and Reasoning (2001)Google Scholar
  13. 13.
    Ozden, B., Ramaswamy, S., Silberschatz, A.: Cyclic Association rule. In: Proceeding of International Conference on Data Engineering, pp. 412–421Google Scholar
  14. 14.
    Chen, X., Petrounias, I.: A framework for temporal data mining. In: Quirchmayr, G., Bench-Capon, T.J.M., Schweighofer, E. (eds.) DEXA 1998. LNCS, vol. 1460, p. 796. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  15. 15.
    Miao, R., Shen, X.-J.: Construction of Periodic Temporal Association Rules in data mining. In: Seventh International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2010). IEEE, Los Alamitos (2010)Google Scholar
  16. 16.
    Winarko, E., Roddick, J.F.: ARMADA – An algorithm for discovering richer relative temporal association rules from interval based data. Data & Knowledge Engineering, 76–90 (2006)Google Scholar
  17. 17.
    Agrwal, R., Srikant, R.: Fast algorithm for mining association rules in large database. In: Proceeding of 20th International Conference on Very Large Databases, pp. 478–499 (1994)Google Scholar
  18. 18.
    Han, J., Pei, J., Vin, V.: Mining frequent pattern without candidate generation. In: Proceedings of 2000 ACM SIGMOD Int. Conference on Management of Data, pp. 486–493 (2000)Google Scholar
  19. 19.
    Han, J., Fu, V.: Discovery of multiple level association rule from large database. In: Proceedings of the 21th International Conference on Very Large Databases, pp. 420–431 (1995)Google Scholar
  20. 20.
    Tung, A.H., Han, J., Lakshmanan, L.S., Ng, R.: Constraints based clustering in large databases. In: Proceeding of 2001 International Conference on Databases Theory (2001)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Mohsin Naqvi
    • 1
  • Kashif Hussain
    • 1
  • Sohail Asghar
    • 1
  • Simon Fong
    • 2
  1. 1.Center of Research in Data Engineering (CORDE)Mohammad Ali Jinnah UniversityIslamabadPakistan
  2. 2.Department of Computer and Information Science, Faculty of Science and TechnologyUniversity of MacauMacau SAR

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