Mining Expressive Temporal Associations from Complex Data

  • Keith A. Pray
  • Carolina Ruiz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3587)

Abstract

We introduce an algorithm for mining expressive temporal relationships from complex data. Our algorithm, AprioriSetsAndSequences (ASAS), extends the Apriori algorithm to data sets in which a single data instance may consist of a combination of attribute values that are nominal sequences, time series, sets, and traditional relational values. Data sets of this type occur naturally in many domains including health care, financial analysis, complex system diagnostics, and domains in which multi-sensors are used. AprioriSetsAndSequences identifies predefined events of interest in the sequential data attributes. It then mines for association rules that make explicit all frequent temporal relationships among the occurrences of those events and relationships of those events and other data attributes. Our algorithm inherently handles different levels of time granularity in the same data set. We have implemented AprioriSetsAndSequences within the Weka environment [1] and have applied it to computer performance, stock market, and clinical sleep disorder data. We show that AprioriSetsAndSequences produces rules that express significant temporal relationships that describe patterns of behavior observed in the data set.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Frank, E., Witten, I.H.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann Publishers, San Francisco (2000)Google Scholar
  2. 2.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proc. of the ACM SIGMOD Conf. on Management of Data, Washington, D.C., pp. 207–216. ACM, New York (1993)Google Scholar
  3. 3.
    Laxminarayan, P., Ruiz, C., Alvarez, S., Moonis, M.: Mining associations over human sleep time series. In: Proc. 18th IEEE Intl. Symposium on Computer-Based Medical Systems, Dublin, Ireland. IEEE, Los Alamitos (2005)Google Scholar
  4. 4.
    Allen, J.: Maintaining knowledge about temporal intervals. Communications of the ACM 26 (1983)Google Scholar
  5. 5.
    Little, J., Rhodes, L.: Understanding Wall Street, 3rd edn. Liberty Hall Press and McGraw-Hill Trade (1991)Google Scholar
  6. 6.
    Shoemaker, C., Ruiz, C.: Association rule mining algorithms for set-valued data. In: Liu, J., Cheung, Y.-m., Yin, H. (eds.) IDEAL 2003. LNCS, vol. 2690, pp. 669–676. Springer, Heidelberg (2003)Google Scholar
  7. 7.
    Stoecker-Sylvia, Z.: Merging the association rule mining modules of the Weka and ARMiner data mining systems. Undergraduate Thesis. WPI (2002)Google Scholar
  8. 8.
    Holmes, S., Leung, C.: Exploring temporal associations in the stock market. Undergraduate Thesis. WPI (2003)Google Scholar
  9. 9.
    Agrawal, R., Srikant, R.: Mining sequential patterns. In: Intl. Conf. on Database Engineering, pp. 3–14. IEEE, Los Alamitos (1995)Google Scholar
  10. 10.
    Zaki, M.: Sequence mining in categorical domains: Incorporating constraints. In: Proc. Intl. Conf. on Information and Knowl. Management (CIKM), pp. 422–429 (2000)Google Scholar
  11. 11.
    Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering Frequent Episodes in Sequences. In: Fayyad, U.M., Uthurusamy, R. (eds.) Proc. of the First Intl. Conf. on Knowledge Discovery and Data Mining (KDD 1995), Montreal, Canada (1995)Google Scholar
  12. 12.
    Mannila, H., Toivonen, H.: Discovering generalized episodes using minimal occurrences. In: Proc. of the Second Intl. Conf. on Knowledge Discovery and Data Mining (KDD 1996), Portland, Oregon, pp. 146–151. AAAI Press, Menlo Park (1996)Google Scholar
  13. 13.
    Das, G., Lin, K.I., Mannila, H., Renganathan, G., Smyth, P.: Rule discovery from time series. In: Proc. of the 4th Intl. Conf. on Knowledge Discovery and Data Mining, pp. 16–22. ACM, New York (1998)Google Scholar
  14. 14.
    Roddick, J., Spiliopoulou, M.: A survey of temporal knowledge discovery paradigms and methods. IEEE Trans. on Knowledge and Data Engineering 14, 750–767 (2002)CrossRefGoogle Scholar
  15. 15.
    Rainsford, C., Roddick, J.: Adding temporal semantics to association rules. In: Żytkow, J.M., Rauch, J. (eds.) PKDD 1999. LNCS (LNAI), vol. 1704, pp. 504–509. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  16. 16.
    Bettini, C., Sean Wang, X., Jajodia, S.: Testing complex temporal relationships involving multiple granularities and its application to data mining. In: Proc. of the Fifteenth ACM Symposium on Principles of Database Systems, pp. 68–78 (1996)Google Scholar
  17. 17.
    Tung, A.K., Lu, H., Han, J., Feng, L.: Efficient mining of intertransaction association rules. IEEE Trans. on Knowledge and Data Engineering, 43–56 (2003)Google Scholar
  18. 18.
    Lu, H., Feng, L., Han, J.: Beyond intratransaction association analysis: mining multidimensional intertransaction association rules. ACM Trans. on Information Systems (TOIS) 18, 423–454 (2000)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Keith A. Pray
    • 1
  • Carolina Ruiz
    • 2
  1. 1.BAE SystemsBurlingtonUSA
  2. 2.Department of Computer ScienceWorcester Polytechnic Institute (WPI)WorcesterUSA

Personalised recommendations