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dc.contributor.authorHahmann, Martin
dc.contributor.authorHabich, Dirk
dc.contributor.authorLehner, Wolfgang
dc.contributor.editorHärder, Theo
dc.contributor.editorLehner, Wolfgang
dc.contributor.editorMitschang, Bernhard
dc.contributor.editorSchöning, Harald
dc.contributor.editorSchwarz, Holger
dc.date.accessioned2019-01-17T10:36:49Z
dc.date.available2019-01-17T10:36:49Z
dc.date.issued2011
dc.identifier.isbn978-3-88579-274-1
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/19625
dc.description.abstractTo benefit from the large amounts of data, gathered in more and more application domains, analysis techniques like clustering have become a necessity. As their application expands, a lot of unacquainted users come into contact with these techniques. Unfortunately, most clustering approaches are complex and/or scenario specific, which makes clustering a challenging domain to access. In this demonstration, we want to present a clustering process, that can be used in a hands-on way.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme für Business, Technologie und Web (BTW)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-180
dc.titleTouch it, mine it, view it, shape iten
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages746-749
mci.conference.sessiontitleRegular Research Papers
mci.conference.locationKaiserslautern
mci.conference.date02.-04.03.2011


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