New algorithms for finding approximate frequent item sets

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BORGELT, Christian, Christian BRAUNE, Tobias KÖTTER, Sonja GRÜN, 2011. New algorithms for finding approximate frequent item sets. In: Soft Computing. 16(5), pp. 903-917. ISSN 1432-7643. eISSN 1433-7479. Available under: doi: 10.1007/s00500-011-0776-2

@article{Borgelt2011algor-23713, title={New algorithms for finding approximate frequent item sets}, year={2011}, doi={10.1007/s00500-011-0776-2}, number={5}, volume={16}, issn={1432-7643}, journal={Soft Computing}, pages={903--917}, author={Borgelt, Christian and Braune, Christian and Kötter, Tobias and Grün, Sonja} }

<rdf:RDF xmlns:dcterms="" xmlns:dc="" xmlns:rdf="" xmlns:bibo="" xmlns:dspace="" xmlns:foaf="" xmlns:void="" xmlns:xsd="" > <rdf:Description rdf:about=""> <dcterms:rights rdf:resource=""/> <dc:creator>Kötter, Tobias</dc:creator> <dc:rights>terms-of-use</dc:rights> <dc:contributor>Braune, Christian</dc:contributor> <dc:creator>Braune, Christian</dc:creator> <dcterms:available rdf:datatype="">2013-06-21T09:40:56Z</dcterms:available> <dcterms:abstract xml:lang="eng">In standard frequent item set mining a transaction supports an item set only if all items in the set are present. However, in many cases this is too strict a requirement that can render it impossible to find certain relevant groups of items. By relaxing the support definition, allowing for some items of a given set to be missing from a transaction, this drawback can be amended. The resulting item sets have been called approximate, fault-tolerant or fuzzy item sets. In this paper we present two new algorithms to find such item sets: the first is an extension of item set mining based on cover similarities and computes and evaluates the subset size occurrence distribution with a scheme that is related to the Eclat algorithm. The second employs a clustering-like approach, in which the distances are derived from the item covers with distance measures for sets or binary vectors and which is initialized with a one-dimensional Sammon projection of the distance matrix. We demonstrate the benefits of our algorithms by applying them to a concept detection task on the 2008/2009 Wikipedia Selection for schools and to the neurobiological task of detecting neuron ensembles in (simulated) parallel spike trains.</dcterms:abstract> <foaf:homepage rdf:resource="http://localhost:8080/jspui"/> <dcterms:title>New algorithms for finding approximate frequent item sets</dcterms:title> <dc:contributor>Borgelt, Christian</dc:contributor> <dc:date rdf:datatype="">2013-06-21T09:40:56Z</dc:date> <dcterms:isPartOf rdf:resource=""/> <dc:creator>Borgelt, Christian</dc:creator> <bibo:uri rdf:resource=""/> <dcterms:issued>2011</dcterms:issued> <dspace:isPartOfCollection rdf:resource=""/> <dc:language>eng</dc:language> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dcterms:bibliographicCitation>Soft Computing ; 16 (2012), 5. - S. 903-917</dcterms:bibliographicCitation> <dc:contributor>Kötter, Tobias</dc:contributor> <dc:creator>Grün, Sonja</dc:creator> <dc:contributor>Grün, Sonja</dc:contributor> </rdf:Description> </rdf:RDF>

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