New algorithms for finding approximate frequent item sets

Zitieren

Dateien zu dieser Ressource

Dateien Größe Format Anzeige

Zu diesem Dokument gibt es keine Dateien.

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

@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:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:dcterms="http://purl.org/dc/terms/" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/rdf/resource/123456789/23713"> <dc:rights>deposit-license</dc:rights> <dc:creator>Kötter, Tobias</dc:creator> <dc:contributor>Braune, Christian</dc:contributor> <dc:creator>Braune, Christian</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">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> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dcterms:title>New algorithms for finding approximate frequent item sets</dcterms:title> <dc:contributor>Borgelt, Christian</dc:contributor> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/23713"/> <dc:creator>Borgelt, Christian</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2013-06-21T09:40:56Z</dc:date> <dcterms:issued>2011</dcterms:issued> <dc:language>eng</dc:language> <dcterms:bibliographicCitation>Soft Computing ; 16 (2012), 5. - S. 903-917</dcterms:bibliographicCitation> <dc:contributor>Kötter, Tobias</dc:contributor> <dc:contributor>Grün, Sonja</dc:contributor> <dc:creator>Grün, Sonja</dc:creator> </rdf:Description> </rdf:RDF>

Das Dokument erscheint in:

KOPS Suche


Stöbern

Mein Benutzerkonto