Abstract
Biomedical experimental data sets may often include many features both at input (description of cases, treatments, or experimental parameters) and output (outcome description). State-of-the-art data mining techniques can deal with such data, but would consider only one output feature at the time, disregarding any dependencies among them. In the paper, we propose the technique that can treat many output features simultaneously, aiming at finding subgroups of cases that are similar both in input and output space. The method is based on k-medoids clustering and analysis of contingency tables, and reports on case subgroups with significant dependency in input and output space. We have used this technique in explorative analysis of clinical data on femoral neck fractures. The subgroups discovered in our study were considered meaningful by the participating domain expert, and sparked a number of ideas for hypothesis to be further experimentally tested.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Hand, D.J.: Handbook of data mining and knowledge discovery. Oxford University Press, Inc., New York (2002)
Fayyad, U., Piatetsky-Shapiro, G., Smyth, P.: The kdd process for extracting useful knowledge from volumes of data. Commun. ACM 39(11), 27–34 (1996)
Lavrač, N., Flach, P., Kavšek, B., Todorovski, L.: Adapting classification rule induction to subgroup discovery. In: Proceedings of IEEE International Conference on Data Mining, pp. 266–273 (2002)
Lavrač, N., Kavšek, B., Flach, P., Todorovski, L.: Subgroup discovery with CN2-SD. Journal of Machine Learning Research 5, 153–188 (2004)
Kavšek, B., Lavrač, N., Jovanoski, V.: APRIORI-SD: Adapting association rule learning to subgroup discovery. In: R. Berthold, M., Lenz, H.-J., Bradley, E., Kruse, R., Borgelt, C. (eds.) IDA 2003. LNCS, vol. 2810, pp. 230–241. Springer, Heidelberg (2003)
Kavšek, B., Lavrač, N.: APRIORI-SD: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence 20(7), 543–583 (2006)
Blockeel, H., De Raedt, L., Ramon, J.: Top-down induction of clustering trees. In: Proceedings of the 15th International Conference on Machine Learning, pp. 55–63. Morgan Kaufmann, San Francisco (1998)
Ženko, B., Struyf, J.: Learning predictive clustering rules. In: Bonchi, F., Boulicaut, J.-F. (eds.) KDID 2005. LNCS, vol. 3933, pp. 234–250. Springer, Heidelberg (2006)
Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. John Wiley, Chichester (1990)
Tibshirani, R., Walther, G., Hastie, T.: Estimating the number of clusters in a dataset via the gap statistic (2000)
Rousseeuw, P.: Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53–65 (1987)
Irigoien, I., Arenas, C.: INCA: new statistic for estimating the number of clusters and identifying atypical units. Statistics in Medicine 27(15), 2948–2973 (2008)
Benjamini, Y., Hochberg, Y.: Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society. Series B (Methodological) 57(1), 289–300 (1995)
Kononenko, I.: Estimating attributes: Analysis and extensions of relief. In: Bergadano, F., De Raedt, L. (eds.) ECML 1994. LNCS, vol. 784, pp. 171–182. Springer, Heidelberg (1994)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Umek, L. et al. (2009). Subgroup Discovery in Data Sets with Multi–dimensional Responses: A Method and a Case Study in Traumatology. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_39
Download citation
DOI: https://doi.org/10.1007/978-3-642-02976-9_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02975-2
Online ISBN: 978-3-642-02976-9
eBook Packages: Computer ScienceComputer Science (R0)