Abstract
This paper addresses the problem of mining rank data, that is, data in the form of rankings (total orders) of an underlying set of items. More specifically, two types of patterns are considered, namely frequent subrankings and dependencies between such rankings in the form of association rules. Algorithms for mining patterns of this kind are proposed and illustrated on three case studies.
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References
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. VLDB, 20th Int. Conf. on Very Large Data Bases, pp. 487–499 (1994)
Boekaerts, M., Smit, K., Busing, F.M.T.A.: Salient goals direct and energise students’ actions in the classroom. Applied Psychology: An International Review 4(S1), 520–539 (2012)
de Sá, C.R., Soares, C., Jorge, A.M., Azevedo, P., Costa, J.: Mining association rules for label ranking. In: Huang, J.Z., Cao, L., Srivastava, J. (eds.) PAKDD 2011, Part II. LNCS, vol. 6635, pp. 432–443. Springer, Heidelberg (2011)
Fagin, R., Kumar, R., Sivakumar, D.: Comparing top-k lists. SIAM Journal of Discrete Mathematics 17(1), 134–160 (2003)
Fürnkranz, J., Hüllermeier, E. (eds.): Preference Learning. Springer (2011)
Han, J., Cheng, H., Xin, D., Yan, X.: Frequent pattern mining: current status and future directions. Data Mining and Knowledge Discovery 15, 55–86 (2007)
Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. ACM SIGMOD Record 29, 1–12 (2000)
Han, J., Pei, J., Yin, Y., Mao, R.: Mining frequent patterns without candidate generation: A frequent-pattern tree approach. Data Mining and Knowledge Discovery 8(1), 53–87 (2004)
Srikant, R., Agrawal, R.: Mining sequential patterns: Generalizations and performance improvements. Springer (1996)
Suzuki, E.: Data mining methods for discovering interesting exceptions from an unsupervised table. J. Universal Computer Science 12(6), 627–653 (2006)
Zaki, M.J.: Scalable algorithms for association mining. IEEE Transactions on Knowledge and Data Engineering 12(3), 372–390 (2000)
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Henzgen, S., Hüllermeier, E. (2014). Mining Rank Data. In: Džeroski, S., Panov, P., Kocev, D., Todorovski, L. (eds) Discovery Science. DS 2014. Lecture Notes in Computer Science(), vol 8777. Springer, Cham. https://doi.org/10.1007/978-3-319-11812-3_11
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DOI: https://doi.org/10.1007/978-3-319-11812-3_11
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11811-6
Online ISBN: 978-3-319-11812-3
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