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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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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