KOPS - Das Institutionelle Repositorium der Universität Konstanz

Evaluation of Hierarchical Interestingness Measures for Mining Pairwise Generalized Association Rules

Evaluation of Hierarchical Interestingness Measures for Mining Pairwise Generalized Association Rules

Zitieren

Dateien zu dieser Ressource

Dateien Größe Format Anzeige

Zu diesem Dokument gibt es keine Dateien.

BENITES, Fernando, Elena SAPOZHNIKOVA, 2014. Evaluation of Hierarchical Interestingness Measures for Mining Pairwise Generalized Association Rules. In: IEEE Transactions on Knowledge and Data Engineering. 26(12), pp. 3012-3025. ISSN 1041-4347. eISSN 1558-2191. Available under: doi: 10.1109/TKDE.2014.2320722

@article{Benites2014Evalu-29269, title={Evaluation of Hierarchical Interestingness Measures for Mining Pairwise Generalized Association Rules}, year={2014}, doi={10.1109/TKDE.2014.2320722}, number={12}, volume={26}, issn={1041-4347}, journal={IEEE Transactions on Knowledge and Data Engineering}, pages={3012--3025}, author={Benites, Fernando and Sapozhnikova, Elena} }

<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/29269"> <dc:creator>Benites, Fernando</dc:creator> <dc:contributor>Benites, Fernando</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-11-21T10:34:04Z</dcterms:available> <dc:contributor>Sapozhnikova, Elena</dc:contributor> <dc:language>eng</dc:language> <dcterms:abstract xml:lang="eng">In the literature about association analysis, many interestingness measures have been proposed to assess the quality of obtained association rules in order to select a small set of the most interesting among them. In the particular case of hierarchically organized items and generalized association rules connecting them, a measure that dealt appropriately with the hierarchy would be advantageous. Here we present the further developments of a new class of such hierarchical interestingness measures and compare them with a large set of conventional measures and with three hierarchical pruning methods from the literature. The aim is to find interesting pairwise generalized association rules connecting the concepts of multiple ontologies. Interested in the broad empirical evaluation of interestingness measures, we compared the rules obtained by 37 methods on four real world data sets against predefined ground truth sets of associations. To this end, we adopted a framework of instance-based ontology matching and extended the set of performance measures by two novel measures: relation learning recall and precision which take into account hierarchical relationships.</dcterms:abstract> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <dc:creator>Sapozhnikova, Elena</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/29269"/> <dcterms:title>Evaluation of Hierarchical Interestingness Measures for Mining Pairwise Generalized Association Rules</dcterms:title> <dcterms:issued>2014</dcterms:issued> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2014-11-21T10:34:04Z</dc:date> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/rdf/resource/123456789/36"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> </rdf:Description> </rdf:RDF>

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto