Hierarchical interestingness measures for association rules with generalization on both antecedent and consequent sides

作者：Benites Fernando^*; Sapozhnikova Elena

来源：Pattern Recognition Letters, 2015, 65: 197-203.

DOI：10.1016/j.patrec.2015.07.027

• Data Mining
• Association rules
• Cross-ontology rules
• Interestingness measures
• Ontology matching

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 theft 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.

• 出版日期2015-11-1