Abstract
The goal of exploratory pattern mining is to find patterns that exhibit yet unknown relationships in data and to provide insightful representations of detected relationships. This paper explores contrast set mining and an approach to improving its explanatory potential by using the so called supporting factors that provide additional descriptions of the detected patterns. The proposed methodology is described in a medical data analysis problem of distinguishing between similar diseases in the analysis of patients suffering from brain ischaemia.
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References
S. Wrobel (1997) An algorithm for multi-relational discovery of subgroups. In Proc. of the First European Conference on Principles of Data Mining and Knowledge Discovery, 1997, pp. 78–87, Springer
Bay S D, Pazzani M J (2001) Detecting group differences: Mining contrast sets. Data Min Knowl Discov 5(3):213–246
Dong G, Li J (1999) Efficient Mining of Emerging Patterns: Discovering Trends and Differences. In Proc. of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, 1999, pp 43–52
Quinlan J R (1993) C4.5: Programs for Machine Learning, Morgan Kaufman Publishers Inc
Clark P, Niblett T (1989) The CN2 induction algorithm. Machine Learning 3(4):261–283, 1989.
Gamberger D, Lavrac Nada, Krstacic G (2003) Active subgroup mining: a case study in coronary heart disease risk group detection. Artif intell med [Print ed.] 28:27–57.
Kralj P, Lavrac N, Gramberger D, Krstacic A (2007) Contrast Set Mining through Subgroup Discovery: Applied to Brain Ischaemina Data. In proc. of the11th Pacific-Asia Conference on Knowledge Discovery and Data Mining, 2007, in press.
Victor M, Ropper A H (2001) Cerebrovascular disease. In Adams and Victor's Principles of Neurology, 2001, pp. 821–924
Fürnkranz J (2001) Round robin rule learning. In Proc. of the 18th International Conference on Machine Learning, 2001, pp 146–153
Demsar J, Zupan B, Leban G (2004) Orange: From Experimental Machine Learning to Interactive Data Mining, White Paper (www.ailab.si/orange), Faculty of Computer and Information Science, University of Ljubljana.
Kavsek B, Lavrac N (2006) APRIORI-SD: Adapting association rule learning to subgroup discovery. Appl artif intell 2006, pp.543–583
Gamberger D, Lavrac N, Krstacic G (2003) Active subgroup mining: a case study in coronary heart disease risk group detection. Artif intell med 28:27–57
Lowry R (2007) Concepts and applications of inferential statistics. http://faculty.vassar.edu/lowry/webtext.html
Lavrac N, Cestnik B, Gamberger D, Flach P (2004) Decision support through subgroup discovery: three case studies and the lessons learned. Mach. learn. [Print ed.], 2004, vol. 57, pp. 115–143.
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Kralj, P., Lavrac, N., Gamberger, D., Krstacic, A. (2007). Supporting Factors to Improve the Explanatory Potential of Contrast Set Mining: Analyzing Brain Ischaemia Data. In: Jarm, T., Kramar, P., Zupanic, A. (eds) 11th Mediterranean Conference on Medical and Biomedical Engineering and Computing 2007. IFMBE Proceedings, vol 16. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73044-6_39
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DOI: https://doi.org/10.1007/978-3-540-73044-6_39
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-73043-9
Online ISBN: 978-3-540-73044-6
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