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Measuring the quality of approximated clusterings

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dc.contributor.authorKriegel, Hans-Peter
dc.contributor.authorPfeifle, Martin
dc.contributor.editorVossen, Gottfried
dc.contributor.editorLeymann, Frank
dc.contributor.editorLockemann, Peter
dc.contributor.editorStucky, Wolffried
dc.date.accessioned2019-10-11T08:35:14Z
dc.date.available2019-10-11T08:35:14Z
dc.date.issued2005
dc.description.abstractClustering has become an increasingly important task in modern application domains. In many areas, e.g. when clustering complex objects, in distributed clustering, or when clustering mobile objects, due to technical, security, or efficiency reasons it is not possible to compute an "optimal" clustering. Recently a lot of research has been done on efficiently computing approximated clusterings. Here, the crucial question is, how much quality has to be sacrificed for the achieved gain in efficiency. In this paper, we present suitable quality measures allowing us to compare approximated clusterings with reference clusterings. We first introduce a quality measure for clusters based on the symmetric set difference. Using this distance function between single clusters, we introduce a quality measure based on the minimum weight perfect matching of sets for comparing partitioning clusterings, as well as a quality measure based on the degree-2 edit distance for comparing hierarchical clusterings.en
dc.identifier.isbn3-88579-394-6
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/28279
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofDatenbanksysteme in Business, Technologie und Web, 11. Fachtagung des GIFachbereichs “Datenbanken und Informationssysteme” (DBIS)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-65
dc.titleMeasuring the quality of approximated clusteringsen
dc.typeText/Conference Paper
gi.citation.endPage424
gi.citation.publisherPlaceBonn
gi.citation.startPage415
gi.conference.date2.-4. März 2005
gi.conference.locationKarlsruhe
gi.conference.sessiontitleRegular Research Papers

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P065 - BTW2005 - Datenbanksysteme in Business, Technologie und Web