Abstract
Exceptional Preferences Mining (EPM) is a crossover between two subfields of datamining: local pattern mining and preference learning. EPM can be seen as a local pattern mining task that finds subsets of observations where the preference relations between subsets of the labels significantly deviate from the norm; a variant of Subgroup Discovery, with rankings as the (complex) target concept. We employ three quality measures that highlight subgroups featuring exceptional preferences, where the focus of what constitutes ‘exceptional’ varies with the quality measure: the first gauges exceptional overall ranking behavior, the second indicates whether a particular label stands out from the rest, and the third highlights subgroups featuring unusual pairwise label ranking behavior. As proof of concept, we explore five datasets. The results confirm that the new task EPM can deliver interesting knowledge. The results also illustrate how the visualization of the preferences in a Preference Matrix can aid in interpreting exceptional preference subgroups.
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References
Abudawood, T., Flach, P.: Evaluation measures for multi-class subgroup discovery. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009, Part I. LNCS (LNAI), vol. 5781, pp. 35–50. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04180-8_20
Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.: Fast discovery of association rules. Adv. Knowl. Disc. Data Min. 12, 307–328 (1996)
Cheng, W., Henzgen, S., Hüllermeier, E.: Labelwise versus pairwise decomposition in label ranking. In: LWA 2013, Lernen, Wissen & Adaptivität, Workshop Proceedings Bamberg, 7–9 October 2013, pp. 129–136 (2013)
Cheng, W., Huhn, J.C., Hüllermeier, E.: Decision tree and instance-based learning for label ranking. In: Proceedings of the 26th Annual International Conference on Machine Learning, ICML 2009, Montreal, Quebec, Canada, 14–18 June 2009, pp. 161–168 (2009)
de Sá, C.R., Soares, C., Knobbe, A.J.: Entropy-based discretization methods for ranking data. Inf. Sci. 329, 921–936 (2016)
Duivesteijn, W.: Exceptional model mining. Ph.D. Thesis, Leiden University (2013)
Duivesteijn, W., Feelders, A., Knobbe, A.: Exceptional model mining. Data Min. Knowl. Disc. 30, 47–98 (2016)
Duivesteijn, W., Knobbe, A.J.: Exploiting false discoveries - statistical validation of patterns and quality measures in subgroup discovery. In: Proceedings of ICDM, pp. 151–160 (2011)
Dzyuba, V., Leeuwen, M.: Interactive discovery of interesting subgroup sets. In: Tucker, A., Höppner, F., Siebes, A., Swift, S. (eds.) IDA 2013. LNCS, vol. 8207, pp. 150–161. Springer, Heidelberg (2013). doi:10.1007/978-3-642-41398-8_14
Fürnkranz, J., Hüllermeier, E.: Pairwise preference learning and ranking. In: Lavrač, N., Gamberger, D., Blockeel, H., Todorovski, L. (eds.) ECML 2003. Lecture Notes in Artificial Intelligence (LNAI), vol. 2837, pp. 145–156. Springer, Heidelberg (2003). doi:10.1007/978-3-540-39857-8_15
Fürnkranz, J., Hüllermeier, E.: Preference Learning, 1st edn. Springer, New York (2010)
Henzgen, S., Hüllermeier, E.: Mining rank data. In: Japkowicz, N., Matwin, S. (eds.) DS 2014. LNCS (LNAI), vol. 8777, pp. 123–134. Springer, Heidelberg (2014). doi:10.1007/978-3-319-11812-3_11
Hüllermeier, E., Fürnkranz, J., Cheng, W., Brinker, K.: Label ranking by learning pairwise preferences. Artif. Intell. 172(16–17), 1897–1916 (2008)
Jin, N., Flach, P., Wilcox, T., Sellman, R., Thumim, J., Knobbe, A.J.: Subgroup discovery in smart electricity meter data. IEEE Trans. Industr. Inf. 10(2), 1327–1336 (2014)
Jorge, A.M., Pereira, F., Azevedo, P.J.: Visual interactive subgroup discovery with numerical properties of interest. In: Japkowicz, N., Matwin, S. (eds.) DS 2006. LNCS (LNAI), vol. 4265, pp. 301–305. Springer, Heidelberg (2006). doi:10.1007/11893318_31
Kamishima, T.: Nantonac collaborative filtering: recommendation based on order responses. In: Proceedings of KDD, pp. 583–588 (2003)
Klösgen, W.: Explora: a multipattern and multistrategy discovery assistant. In: Advances in Knowledge Discovery and Data Mining, pp. 249–271 (1996)
Klösgen, W., Zytkow, J.M. (eds.): Handbook of Data Mining and Knowledge Discovery. Oxford University Press, New York (2002)
Van, T., Leeuwen, M., Nijssen, S., Fierro, A.C., Marchal, K., Raedt, L.: Ranked tiling. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8725, pp. 98–113. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44851-9_7
Lichman, M.: UCI Machine Learning Repository (2013)
Mannila, H., Toivonen, H.: Levelwise search and borders of theories in knowledge discovery. Data Min. Knowl. Discov. 1(3), 241–258 (1997)
Meeng, M., Knobbe, A.: Flexible enrichment with cortana-software demo. In: Proceedings of BeneLearn, pp. 117–119 (2011)
Umek, L., Zupan, B.: Subgroup discovery in data sets with multi-dimensional responses. Intell. Data Anal. 15(4), 533–549 (2011)
Vembu, S., Gärtner, T.: Label ranking algorithms: a survey. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning, pp. 45–64. Springer, Heidelberg (2010)
Acknowledgments
This work was supported by the European Union through the ERC Consolidator Grant FORSIED (project reference 615517). This work was also financed by the ERDF - European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project POCI-01-0145-FEDER-006961 and by National Funds through the FCT - Fundação para a Ciência e a Tecnologia (Portuguese Foundation for Science and Technology) as part of project UID/EEA/50014/2013.
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Rebelo de Sá, C., Duivesteijn, W., Soares, C., Knobbe, A. (2016). Exceptional Preferences Mining. In: Calders, T., Ceci, M., Malerba, D. (eds) Discovery Science. DS 2016. Lecture Notes in Computer Science(), vol 9956. Springer, Cham. https://doi.org/10.1007/978-3-319-46307-0_1
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