Mining Patterns from Longitudinal Studies

  • Aída Jiménez
  • Fernando Berzal
  • Juan-Carlos Cubero
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7121)


Longitudinal studies are observational studies that involve repeated observations of the same variables over long periods of time. In this paper, we propose the use of tree pattern mining techniques to discover potentially interesting patterns within longitudinal data sets. Following the approach described in [15], we propose four different representation schemes for longitudinal studies and we analyze the kinds of patterns that can be identified using each one of the proposed representation schemes. Our analysis provides some practical guidelines that might be useful in practice for exploring longitudinal datasets.


Representation Scheme Longitudinal Data Analysis Longitudinal Dataset Sibling Node Longitudinal Database 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abe, K., Kawasoe, S., Asai, T., Arimura, H., Arikawa, S.: Efficient substructure discovery from large semi-structured data. In: Proceedings of the 2nd SIAM International Conference on Data Mining (2002)Google Scholar
  2. 2.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules in large databases. In: Proceedings of 20th International Conference on Very Large Data Bases, September 12-15, pp. 487–499 (1994)Google Scholar
  3. 3.
    Alati, R., O’Callaghan, M., Najman, J.M., Williams, G.M., Bor, W., Lawlor, D.A.: Asthma and internalizing behavior problems in adolescence: A longitudinal study. Psychosomatic Medicine 67(3), 462–470 (2005)CrossRefGoogle Scholar
  4. 4.
    Asai, T., Arimura, H., Uno, T., Nakano, S.-i.: Discovering Frequent Substructures in Large Unordered Trees. In: Grieser, G., Tanaka, Y., Yamamoto, A. (eds.) DS 2003. LNCS (LNAI), vol. 2843, pp. 47–61. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  5. 5.
    Chi, Y., Yang, Y., Muntz, R.R.: HybridTreeMiner: An efficient algorithm for mining frequent rooted trees and free trees using canonical form. In: Proceedings of the 16th International Conference on Scientific and Statistical Database Management. pp. 11–20 (2004)Google Scholar
  6. 6.
    Cothey, V.: A longitudinal study of world wide web users’ information-searching behavior. Journal of the American Society for Information Science and Technology 53(2), 67–78 (2002)CrossRefGoogle Scholar
  7. 7.
    Diggle, P.J., Liang, K.Y., Zeger, S.L.: Analysis of longitudinal data. Oxford Statistical Science Series, vol. 13. Clarendon Press (1994)Google Scholar
  8. 8.
    Fitzmaurice, G.M., Laird, N.M., Ware, J.H.: Applied Longitudinal Analysis. Wiley Series in Probability and Statistics (2004)Google Scholar
  9. 9.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1–12 (2000)Google Scholar
  10. 10.
    Harada, S., Wobbrock, J.O., Malkin, J., Bilmes, J.A., Landay, J.A.: Longitudinal study of people learning to use continuous voice-based cursor control. In: Proceedings of the 27th International Conference on Human Factors in Computing Systems, pp. 347–356 (2009)Google Scholar
  11. 11.
    Hedeker, D., Gibbons, R.D.: Longitudinal Data Analysis. Wiley-Interscience (2006)Google Scholar
  12. 12.
    Hido, S., Kawano, H.: AMIOT: Induced ordered tree mining in tree-structured databases. In: Proceedings of the 5th IEEE International Conference on Data Mining, pp. 170–177 (2005)Google Scholar
  13. 13.
    Jiménez, A., Berzal, F., Cubero, J.C.: Frequent tree pattern mining: A survey. Intelligent Data Analysis 14, 603–622 (2010)Google Scholar
  14. 14.
    Jiménez, A., Berzal, F., Cubero, J.C.: POTMiner: Mining ordered, unordered, and partially-ordered trees. Knowlegde and Information Systems 23(2), 199–224 (2010)CrossRefGoogle Scholar
  15. 15.
    Jiménez, A., Berzal, F., Cubero, J.C.: Using trees to mine multirelational databases. In: Data Mining and Knowledge Discovery pp. 1–39 (2011),
  16. 16.
    Lee, K., Mercante, D.: Longitudinal nominal data analysis using marginalized models. Computational Statistics & Data Analysis 54(1), 208–218 (2010)CrossRefzbMATHGoogle Scholar
  17. 17.
    Nijssen, S., Kok, J.N.: Efficient discovery of frequent unordered trees. In: First International Workshop on Mining Graphs, Trees and Sequences (MGTS 2003), in conjunction with ECML/PKDD 2003, pp. 55–64 (2003)Google Scholar
  18. 18.
    Raudenbush, S.W., Shing Chan, W.: Growth curve analysis in accelerated longitudinal designs. Journal of Research in Crime and Delinquency 29(4), 387–411 (1992)CrossRefGoogle Scholar
  19. 19.
    Roberts, B.W., DelVecchio, W.F.: The rank-order consistency of personality traits from childhood to old age: A quantitative review of longitudinal studies. Psychological Bulletin 126(1), 3–25 (2000)CrossRefGoogle Scholar
  20. 20.
    Singer, J.D., Willett, J.B.: Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press (2003)Google Scholar
  21. 21.
    de Vries, H., van’t Riet, J., Spigt, M., Metsemakers, J., van den Akker, M., Vermunt, J.K., Kremers, S.: Clusters of lifestyle behaviors: Results from the Dutch SMILE study. Preventive Medicine 46(3), 203–208 (2008)CrossRefGoogle Scholar
  22. 22.
    Wang, C., Hong, M., Pei, J., Zhou, H., Wang, W., Shi, B.-L.: Efficient Pattern-Growth Methods for Frequent Tree Pattern Mining. In: Dai, H., Srikant, R., Zhang, C. (eds.) PAKDD 2004. LNCS (LNAI), vol. 3056, pp. 441–451. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  23. 23.
    Xiao, Y., Yao, J.F., Li, Z., Dunham, M.H.: Efficient data mining for maximal frequent subtrees. In: Proceedings of the 3rd IEEE International Conference on Data Mining, pp. 379–386 (2003)Google Scholar
  24. 24.
    Zaki, M.J.: Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae 66(1-2), 33–52 (2005)zbMATHGoogle Scholar
  25. 25.
    Zaki, M.J.: Efficiently mining frequent trees in a forest: Algorithms and applications. IEEE Transactions on Knowledge and Data Engineering 17(8), 1021–1035 (2005)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Aída Jiménez
    • 1
  • Fernando Berzal
    • 1
  • Juan-Carlos Cubero
    • 1
  1. 1.Department of Computer Science and Artificial IntelligenceCITIC, University of GranadaGranadaSpain

Personalised recommendations