Abstract
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.
The work described in this paper has been partially supported by the TIN2009-08296 research project from the Spanish Ministry of Science and Innovation.
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References
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)
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)
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)
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)
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)
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)
Diggle, P.J., Liang, K.Y., Zeger, S.L.: Analysis of longitudinal data. Oxford Statistical Science Series, vol. 13. Clarendon Press (1994)
Fitzmaurice, G.M., Laird, N.M., Ware, J.H.: Applied Longitudinal Analysis. Wiley Series in Probability and Statistics (2004)
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)
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)
Hedeker, D., Gibbons, R.D.: Longitudinal Data Analysis. Wiley-Interscience (2006)
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)
Jiménez, A., Berzal, F., Cubero, J.C.: Frequent tree pattern mining: A survey. Intelligent Data Analysis 14, 603–622 (2010)
Jiménez, A., Berzal, F., Cubero, J.C.: POTMiner: Mining ordered, unordered, and partially-ordered trees. Knowlegde and Information Systems 23(2), 199–224 (2010)
Jiménez, A., Berzal, F., Cubero, J.C.: Using trees to mine multirelational databases. In: Data Mining and Knowledge Discovery pp. 1–39 (2011), http://dx.doi.org/10.1007/s10618-011-0218-x
Lee, K., Mercante, D.: Longitudinal nominal data analysis using marginalized models. Computational Statistics & Data Analysis 54(1), 208–218 (2010)
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)
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)
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)
Singer, J.D., Willett, J.B.: Applied Longitudinal Data Analysis: Modeling Change and Event Occurrence. Oxford University Press (2003)
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)
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)
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)
Zaki, M.J.: Efficiently mining frequent embedded unordered trees. Fundamenta Informaticae 66(1-2), 33–52 (2005)
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)
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Jiménez, A., Berzal, F., Cubero, JC. (2011). Mining Patterns from Longitudinal Studies. In: Tang, J., King, I., Chen, L., Wang, J. (eds) Advanced Data Mining and Applications. ADMA 2011. Lecture Notes in Computer Science(), vol 7121. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-25856-5_13
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DOI: https://doi.org/10.1007/978-3-642-25856-5_13
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