Abstract
We introduce the notion of an association reduct. It is an analogy to association rules at the level of global dependencies between the sets of attributes. Association reducts represent important complex relations, beyond usually considered “single attribute – single attribute” similarities. They can also express approximate dependencies in terms of, for instance, the information-theoretic measures. Finally, association reducts can be extracted from data using algorithms adapted from the domain of association rules and the theory of rough sets.
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Ślezak, D. (2005). Association Reducts: A Framework for Mining Multi-attribute Dependencies. In: Hacid, MS., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds) Foundations of Intelligent Systems. ISMIS 2005. Lecture Notes in Computer Science(), vol 3488. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11425274_37
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DOI: https://doi.org/10.1007/11425274_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25878-0
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