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- W1862882477 abstract "Hierarchical interesting measures for generalized association rules are studied.An extension of such measures to generalization on both sides of a rule is proposed.Two of three extended hierarchical measures found more true rules in the experiments. Pairwise generalized association rules mined from raw data can be used to connect the concepts of multiple ontologies. In this case the items of rules are hierarchically organized and one can use the relations between them in order to reduce rule redundancy. Recently proposed hierarchical interestingness measures address this issue, taking hierarchical information on the antecedent side into account. In this paper, we extend them to the case of considering two hierarchies on both the antecedent and the consequent sides of a rule. The extended measures are then compared with their counterparts as well as with conventional ones. Three real world datasets from the text mining domain with predefined ground truth sets of associations are used for comparison within the framework of instance-based ontology mapping." @default.
- W1862882477 created "2016-06-24" @default.
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- W1862882477 date "2015-11-01" @default.
- W1862882477 modified "2023-09-26" @default.
- W1862882477 title "Hierarchical interestingness measures for association rules with generalization on both antecedent and consequent sides" @default.
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- W1862882477 doi "https://doi.org/10.1016/j.patrec.2015.07.027" @default.
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