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City Philadelphia / Heraklion / Berlin / Portland / Seattle / Seoul / Lake Buena Vista / Sydney / / Company Distributed Data Systems / Mei / Astronomical Data Analysis Software / / Country Germany / United States / Australia / Korea / Greece / / / Facility Martin Pfeifle University of Munich / / IndustryTerm non-effective clustering algorithms / transportation problems / und Web / hierarchical clustering algorithms / sensor networks / approximated clustering algorithms / density-based clustering algorithms / hierarchical algorithms / data mining method / real time information / density-based partitioning clustering algorithm / data mining algorithm / wide area networks / proposed data mining algorithms / distributed mobile networks / server site / data mining algorithms / hierarchical clustering algorithm / approximated data mining algorithms / suitable cluster recognition algorithms / / OperatingSystem Microsoft Vista / / Organization American Statistical Association / Martin Pfeifle University of Munich / Institute for Computer Science / / Person Buena Vista / Hans-Peter Kriegel / / Position cluster representative / / ProgrammingLanguage FL / C / / ProvinceOrState Pennsylvania / Oregon / Florida / / PublishedMedium Machine Learning / / RadioStation FM 83 / / Technology expensive data mining algorithms / hierarchical clustering algorithm / suitable cluster recognition algorithms / Constant Time Approximation Algorithms / Hierarchical clustering algorithms / clustering algorithms / approximated clustering algorithms / proposed data mining algorithms / density-based partitioning clustering algorithm / data mining algorithms / approximated data mining algorithms / Density-Based Algorithm / density-based clustering algorithms / KDD algorithms / data mining / Machine Learning / expensive hierarchical algorithms / data mining algorithm / partitioning clustering algorithm / / URL http /
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11. GI-Fachtagung Datenbanksysteme für Business, Technologie und Web (BTW), Springer Lecture Notes in Informatics (LNI), 2005. Measuring the Quality of Approximated Clusterings Hans-Peter Kriegel, Martin Pfeifle Univers