An Efficient Approach to Clustering in Large Multimedia Databases with Noise

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HINNEBURG, Alexander, Daniel A. KEIM, 1998. An Efficient Approach to Clustering in Large Multimedia Databases with Noise. Knowledge Discovery and Datamining (KDD'98). New York, NY, 1998. In: Proceedings of the 4 th International Conference on Knowledge Discovery and Datamining (KDD ' 98), New York, NY, September, 1998. Knowledge Discovery and Datamining (KDD'98). New York, NY, 1998, pp. 58-65

@inproceedings{Hinneburg1998Effic-5816, title={An Efficient Approach to Clustering in Large Multimedia Databases with Noise}, year={1998}, booktitle={Proceedings of the 4 th International Conference on Knowledge Discovery and Datamining (KDD ' 98), New York, NY, September, 1998}, pages={58--65}, author={Hinneburg, Alexander and Keim, Daniel A.} }

<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/123456789/5816"> <dcterms:abstract xml:lang="eng">Several clustering algorithms can be applied to clustering in large multimedia databases. The effectiveness and efficiency of the existing algorithms, however, is somewhat limited, since clustering in multimedia databases requires clustering high-dimensional feature vectors and since multimedia databases often contain large amounts of noise. In this paper, we therefore introduce a new algorithm to clustering in large multimedia databases called DENCLUE (DENsitybased CLUstEring). The basic idea of our new approach is to model the overall point density analytically as the sum of influence functions of the data points. Clusters can then be identified by determining density-at tractors and clusters of arbitrary shape can be easily described by a simple equation based on the overall density function. The advantages of our new approach are (1) it has a finn mathematical basis, (2) it has good clustering properties in data sets with large amounts of noise, (3) it allows a compact mathematical description of arbitrarily shaped clusters in high-dimensional data sets and (4) it is significantly faster than existing algorithms. To demonstrate the effectiveness and efficiency of DENCLUE, we perform a series of experiments on a number of different data sets from CAD and molecular biology. A comparison with DBSCAN shows the superiority of our new approach.</dcterms:abstract> <dcterms:rights rdf:resource="https://creativecommons.org/licenses/by-nc-nd/2.0/legalcode"/> <dc:creator>Hinneburg, Alexander</dc:creator> <dc:rights>deposit-license</dc:rights> <dc:format>application/pdf</dc:format> <dcterms:bibliographicCitation>First publ. in: Proceedings of the 4th International Conference on Knowledge Discovery and Datamining (KDD'98), New York, NY, September, 1998, pp. 58-65</dcterms:bibliographicCitation> <dc:language>eng</dc:language> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T16:00:21Z</dc:date> <dc:contributor>Keim, Daniel A.</dc:contributor> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5816"/> <dc:contributor>Hinneburg, Alexander</dc:contributor> <dcterms:title>An Efficient Approach to Clustering in Large Multimedia Databases with Noise</dcterms:title> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:issued>1998</dcterms:issued> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T16:00:21Z</dcterms:available> </rdf:Description> </rdf:RDF>

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