Abstract
The task addressed and the method proposed in this paper aim at improved understanding of differences between similar diseases. In particular we address the problem of distinguishing between thrombolic brain stroke and embolic brain stroke as an application of our approach of contrast set mining through subgroup discovery. We describe methodological lessons learned in the analysis of brain ischaemia data and a practical implementation of the approach within an open source data mining toolbox.
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References
Bay, S.D., Pazzani, M.J.: Detecting group differences: Mining contrast sets. Data Mining and Knowledge Discovery 5(3), 213–246 (2001)
Demšar, J., Zupan, B., Leban, G.: Orange: From experimental machine learning to interactive data mining, white paper. Faculty of Computer and Information Science, University of Ljubljana (2004) www.ailab.si/orange
Fürnkranz, J.: Round robin rule learning. In: ICML 2001: Proceedings of the Eighteenth International Conference on Machine Learning, pp. 146–153 (2001)
Gramberger, D., Lavrač, N.: Expert-guided subgroup discovery: Methodology and application. Journal of Artificial Intelligence Research 17, 501–527 (2002)
Jovanovski, V., Lavrač, N.: Classification rule learning with APRIORI-C. In: Proceedings of the 10th Portuguese Conference on Artificial Intelligence, pp. 44–51 (2001)
Kavšek, B., Lavrač, N.: APRIORI-SD: Adapting association rule learning to subgroup discovery. Applied Artificial Intelligence, 543–583 (2006)
Kralj, P., Lavrač, N., Gramberger, D., Krstačić, A.: Contrast set mining through subgroup discovery applied to brain ischaemia data. In: PAKDD 2007: Proceedings of the 11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, Springer, Heidelberg (2007)
Kralj, P., Lavrač, N., Zupan, B.: Subgroup visualization. In: IS 2005: Proceedings of the 8th International Multiconference Information Society, pp. 228–231 (2005)
Kralj, P., Lavrač, N., Zupan, B., Gramberger, D.: Experimental comparison of three subgroup discovery algorithms: Analysing brain ischemia data. In: IS 2005: Proceedings of the 8th International Multiconference Information Society, pp. 220–223 (2005)
Lavrač, N., Kavšek, B., Flach, P., Todorovski, L.: Subgroup discovery with CN2-SD. Journal of Machine Learning Research 5, 153–188 (2004)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufman Publishers Inc, San Francisco (1993)
Victor, M., Ropper, A.H.: Cerebrovascular disease. Adams and Victor’s Principles of Neurology, 821–924 (2001)
Webb, G.I., Butler, S., Newlands, D.: On detecting differences between groups. In: KDD 2003: Proceedings of the 9th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 256–265 (2003)
Wrobel, S.: An algorithm for multi-relational discovery of subgroups. In: Proceedings of the 1st European Conference on Principles of Data Mining and Knowledge Discovery, pp. 78–87. Springer, Heidelberg (1997)
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Kralj, P., Lavrač, N., Gamberger, D., Krstačić, A. (2007). Contrast Set Mining for Distinguishing Between Similar Diseases. In: Bellazzi, R., Abu-Hanna, A., Hunter, J. (eds) Artificial Intelligence in Medicine. AIME 2007. Lecture Notes in Computer Science(), vol 4594. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73599-1_12
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DOI: https://doi.org/10.1007/978-3-540-73599-1_12
Publisher Name: Springer, Berlin, Heidelberg
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