Abstract
Problems of discovering association rules in data sets containing semantic information about preference orders on domains of attributes are considered. Such attributes are called criteria and they are typically present in data related to economic issues, like financial or marketing data. We introduce a specific form of association rules involving criteria. Discovering such rules requires new concepts: semantic correlation of criteria, inconsistency of objects with respect to the dominance, credibility index. Properties of these rules concerning their generality and interdependencies are studied. We also sketch the way of mining such rules.
Keywords
- Association Rule
- Frequent Itemsets
- Atomic Formula
- Mining Association Rule
- Mining Association
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2002 Springer-Verlag Berlin Heidelberg
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Greco, S., Slowinski, R., Stefanowski, J. (2002). Mining Association Rules in Preference-Ordered Data. In: Hacid, MS., Raś, Z.W., Zighed, D.A., Kodratoff, Y. (eds) Foundations of Intelligent Systems. ISMIS 2002. Lecture Notes in Computer Science(), vol 2366. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48050-1_48
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DOI: https://doi.org/10.1007/3-540-48050-1_48
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Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43785-7
Online ISBN: 978-3-540-48050-1
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