An Iterative Method for Mining Frequent Temporal Patterns

  • Francisco Guil
  • Antonio Bailón
  • Alfonso Bosch
  • Roque Marín
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3643)

Abstract

The incorporation of temporal semantic into the traditional data mining techniques has caused the creation of a new area called Temporal Data Mining. This incorporation is especially necessary if we want to extract useful knowledge from dynamic domains, which are time-varying in nature. However, this process is computationally complex, and therefore it poses more challenges on efficient processing that non-temporal techniques. Based in the inter-transactional framework, in [11] we proposed an algorithm named TSET for mining temporal patterns (sequences) from datasets which uses a unique tree-based structure for storing all frequent patterns discovered in the mining process. However, in each data mining process, the algorithm must generate the whole structure from scratch. In this work, we propose an extension which consists in the reusing of structures generated in previous data mining process in order to reduce the execution time of the algorithm.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Aggarwal, C.C.: Towards long pattern generation in dense databases. SIGKDD Explorations 3(1), 20–26 (2001)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.N.: Mining association rules between sets of items in large databases. In: Buneman, P., Jajodia, S. (eds.) Proc. of the ACM SIGMOD Int. Conf. on Management of Data, Washington, D.C., May 26-28, pp. 207–216. ACM Press, New York (1993)Google Scholar
  3. 3.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proc. of 20th Int. Conf. on Very Large Data Bases (VLDB 1994), Santiago de Chile, Chile, September 12-15, pp. 487–499. Morgan Kaufmann, San Francisco (1994)Google Scholar
  4. 4.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Yu, P.S., Chen, A.L.P. (eds.) Proc. of the 11th Int. Conf. on Data Engineering, Taipei, Taiwan, March 6-10, pp. 3–14. IEEE Computer Society, Los Alamitos (1995)CrossRefGoogle Scholar
  5. 5.
    Ale, J.M., Rossi, G.H.: An approach to discovering temporal association rules. In: Proc. of the 2000 ACM Symposium on Applied Computing, Villa Olmo, Via Cantoni 1, 22100 Como, Italy, March 19-21, pp. 294–300. ACM, New York (2000)Google Scholar
  6. 6.
    Bayardo, R.J.: Efficiently mining long patterns from databases. In: Haas, L.M., Tiwary, A. (eds.) Proc. of the ACM SIGMOD Int. Conf. on Management of Data (SIGMOD 1998), Seattle, Washington, USA, June 2-4, pp. 85–93. ACM Press, New York (1998)CrossRefGoogle Scholar
  7. 7.
    Bettini, C., Wang, X.S., Jajodia, S.: Testing complex temporal relationships involving multiple granularities and its application to data mining. In: Proc. of the 15th ACM SIGACT-SIGMOD-SIGART Symposium on Principles of Database Systems, Montreal, Canada, June 3-5, pp. 68–78. ACM Press, New York (1996)Google Scholar
  8. 8.
    Coenen, F., Goulbourne, G., Leng, P.: Tree structures for mining association rules. Data Mining and Knowledge Discovery 8, 25–51 (2004)CrossRefMathSciNetGoogle Scholar
  9. 9.
    Fayyad, U., Piatetky-Shapiro, G., Smyth, P.: From data mining to knowledge discovery in databases. AIMagazine 17(3), 37–54 (1996)Google Scholar
  10. 10.
    Feng, L., Yu, J.X., Lu, H., Han, J.: A template model for multidimensional inter-transactional association rules. The VLDB Journal 11, 153–175 (2002)CrossRefGoogle Scholar
  11. 11.
    Guil, F., Bosch, A., Marín, R.: TSET: An algorithm for mining frequent temporal patterns. In: Proc. of the First Int. Workshop on Knowledge Discovery in Data Streams, in conjunction with ECML/PKDD 2004, pp. 65–74 (2004)Google Scholar
  12. 12.
    Lee, C.H., Lin, C.R., Chen, M.S.: On mining general temporal association rules in a publication database. In: Cercone, N., Lin, T.Y., Wu, X. (eds.) Proc. of the 2001 IEEE Int. Conf. on Data Mining, San Jose, California, USA, November 29-December 2, pp. 337–344. IEEE Computer Society, Los Alamitos (2001)Google Scholar
  13. 13.
    Lee, J.W., Lee, Y.J., Kim, H.K., Hwang, B.H., Ryu, K.H.: Discovering temporal relation rules mining from interval data. In: Shafazand, H., Tjoa, A.M. (eds.) EurAsia-ICT 2002. LNCS, vol. 2510, pp. 57–66. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  14. 14.
    Li, Y., Ning, P., Wang, X.S., Jajodia, S.: Discovering calendar-based temporal association rules. Data & Knowledge Engineering 44, 193–218 (2003)CrossRefGoogle Scholar
  15. 15.
    Lu, H., Feng, L., Han, J.: Beyond intra-transaction association analysis: Mining multi-dimensional inter-transaction association rules. ACM Transactions on Information Systems (TOIS) 18(4), 423–454 (2000)CrossRefGoogle Scholar
  16. 16.
    Lu, H., Han, J., Feng, L.: Stock movement and n-dimensional inter-transaction association rules. In: Proc. of the Workshop on Research Issues on Data Mining and Knowledge Discovery (DMKD 1998), Seattle, Washington, June 1998, pp. 12:1–12:7(1998)Google Scholar
  17. 17.
    Mannila, H.: Local and global methods in data mining: Basic techniques and open problems. In: Widmayer, P., Triguero, F., Morales, R., Hennessy, M., Eidenbenz, S., Conejo, R. (eds.) ICALP 2002. LNCS, vol. 2380, pp. 57–68. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  18. 18.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovery of frequent episodes in event sequences. Data Mining and Knowledge Discovery 1(3), 259–289 (1997)CrossRefGoogle Scholar
  19. 19.
    Ordonez, C., Santana, C.A., de Braal, L.: Discovering interesting association rules in medical data. In: Gunopulos, D., Rastogi, R. (eds.) Proc. of the ACM SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, Dallas, Texas, USA, May 14, pp. 78–85 (2000)Google Scholar
  20. 20.
    Özden, B., Ramaswamy, S., Silberschatz, A.: Cyclic association rules. In: Proc. of the 14th Int. Conf. on Data Engineering, Orlando, Florida, USA, February 23-27, pp. 412–421. IEEE Computer Society, Los Alamitos (1998)CrossRefGoogle Scholar
  21. 21.
    Pani, A.K.: Temporal representation and reasoning in artificial intelligence: A review. Mathematical and Computer Modelling 34, 55–80 (2001)MATHCrossRefGoogle Scholar
  22. 22.
    Roddick, J.F., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. IEEE Transactions on Knowledge and Data Engineering 14(4), 750–767 (2002)CrossRefGoogle Scholar
  23. 23.
    Tung, A.K.H., Lu, H., Han, J., Feng, L.: Breaking the barrier of transactions: Mining inter-transaction association rules. In: Proc. of the 5th ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, San Diego, CA, USA, August 15-18, pp. 297–301. ACM Press, New York (1999)CrossRefGoogle Scholar
  24. 24.
    Tung, A.K.H., Lu, H., Han, J., Feng, L.: Efficient mining of intertransaction association rules. IEEE Transactions on Knowledge and Data Engineering 15(1), 43–56 (2003)CrossRefGoogle Scholar
  25. 25.
    Zhou, Z.H.: Three perspectives of data mining (book review). Artificial Intelligence 143, 139–146 (2003)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Francisco Guil
    • 1
  • Antonio Bailón
    • 2
  • Alfonso Bosch
    • 1
  • Roque Marín
    • 3
  1. 1.Departamento de Lenguajes y ComputaciónUniversidad de AlmeríaAlmería
  2. 2.Dept. Ciencias de la Computación e Inteligencia ArtificialUniversidad de GranadaGranada
  3. 3.Dept. Ingeniería de la Información y las ComunicacionesUniversidad de MurciaEspinardo (Murcia)

Personalised recommendations