Publikation: Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering
Dateien
Datum
Autor:innen
Herausgeber:innen
ISSN der Zeitschrift
Electronic ISSN
ISBN
Bibliografische Daten
Verlag
Schriftenreihe
Auflagebezeichnung
URI (zitierfähiger Link)
Internationale Patentnummer
Link zur Lizenz
Angaben zur Forschungsförderung
Projekt
Open Access-Veröffentlichung
Core Facility der Universität Konstanz
Titel in einer weiteren Sprache
Publikationstyp
Publikationsstatus
Erschienen in
Zusammenfassung
Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work effectively and efficiently in high-dimensional space, which is due to the so-called "curse of dimensionality". In addition, the high-dimensional data often contains a significant amount of noise which causes additional effectiveness problems. In this paper, we review and compare the existing algorithms for clustering high-dimensional data and show the impact of the curse of dimensionality on their effectiveness and efficiency. The comparison reveals that condensation-based approaches (such as BIRCH or STING) are the most promising candidates for achieving the necessary efficiency, but it also shows that basically all condensation-based approaches have severe weaknesses with respect to their effectiveness in high-dimensional space. To overcome these problems, we develop a new clustering technique called OptiGrid which is based on constructing an optimal grid-partitioning of the data. The optimal grid-partitioning is determined by calculating the best partitioning hyperplanes for each dimension (if such a partitioning exists) using certain projections of the data. The advantages of our new approach are (1) it has a firm mathematical basis (2) it is by far more effective than existing clustering algorithms for high-dimensional data (3) it is very efficient even for large data sets of high dimensionality. To demonstrate the effectiveness and efficiency of our new approach, we perform a series of experiments on a number of different data sets including real data sets from CAD and molecular biology. A comparison with one of the best known algorithms (BIRCH) shows the superiority of our new approach.
Zusammenfassung in einer weiteren Sprache
Fachgebiet (DDC)
Schlagwörter
Konferenz
Rezension
Zitieren
ISO 690
HINNEBURG, Alexander, Daniel A. KEIM, 1999. Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering. In: Proceedings of the 25 th International Conference on Very Large Databases, 1999. 1999, pp. 506-517BibTex
@inproceedings{Hinneburg1999Optim-5790, year={1999}, title={Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering}, booktitle={Proceedings of the 25 th International Conference on Very Large Databases, 1999}, pages={506--517}, author={Hinneburg, Alexander and Keim, Daniel A.} }
RDF
<rdf:RDF xmlns:dcterms="http://purl.org/dc/terms/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:bibo="http://purl.org/ontology/bibo/" xmlns:dspace="http://digital-repositories.org/ontologies/dspace/0.1.0#" xmlns:foaf="http://xmlns.com/foaf/0.1/" xmlns:void="http://rdfs.org/ns/void#" xmlns:xsd="http://www.w3.org/2001/XMLSchema#" > <rdf:Description rdf:about="https://kops.uni-konstanz.de/server/rdf/resource/123456789/5790"> <foaf:homepage rdf:resource="http://localhost:8080/"/> <dc:creator>Keim, Daniel A.</dc:creator> <dcterms:abstract xml:lang="eng">Many applications require the clustering of large amounts of high-dimensional data. Most clustering algorithms, however, do not work effectively and efficiently in high-dimensional space, which is due to the so-called "curse of dimensionality". In addition, the high-dimensional data often contains a significant amount of noise which causes additional effectiveness problems. In this paper, we review and compare the existing algorithms for clustering high-dimensional data and show the impact of the curse of dimensionality on their effectiveness and efficiency. The comparison reveals that condensation-based approaches (such as BIRCH or STING) are the most promising candidates for achieving the necessary efficiency, but it also shows that basically all condensation-based approaches have severe weaknesses with respect to their effectiveness in high-dimensional space. To overcome these problems, we develop a new clustering technique called OptiGrid which is based on constructing an optimal grid-partitioning of the data. The optimal grid-partitioning is determined by calculating the best partitioning hyperplanes for each dimension (if such a partitioning exists) using certain projections of the data. The advantages of our new approach are (1) it has a firm mathematical basis (2) it is by far more effective than existing clustering algorithms for high-dimensional data (3) it is very efficient even for large data sets of high dimensionality. To demonstrate the effectiveness and efficiency of our new approach, we perform a series of experiments on a number of different data sets including real data sets from CAD and molecular biology. A comparison with one of the best known algorithms (BIRCH) shows the superiority of our new approach.</dcterms:abstract> <dc:contributor>Keim, Daniel A.</dc:contributor> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T16:00:07Z</dcterms:available> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/5790"/> <dcterms:hasPart rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5790/1/vldb99.pdf"/> <void:sparqlEndpoint rdf:resource="http://localhost/fuseki/dspace/sparql"/> <dspace:hasBitstream rdf:resource="https://kops.uni-konstanz.de/bitstream/123456789/5790/1/vldb99.pdf"/> <dcterms:rights rdf:resource="http://creativecommons.org/licenses/by-nc-nd/2.0/"/> <dspace:isPartOfCollection rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dcterms:issued>1999</dcterms:issued> <dcterms:bibliographicCitation>First publ. in: Proceedings of the 25th International Conference on Very Large Databases, 1999, pp. 506-517</dcterms:bibliographicCitation> <dcterms:title>Optimal Grid-Clustering : Towards Breaking the Curse of Dimensionality in High-Dimensional Clustering</dcterms:title> <dc:creator>Hinneburg, Alexander</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2011-03-24T16:00:07Z</dc:date> <dc:format>application/pdf</dc:format> <dc:rights>Attribution-NonCommercial-NoDerivs 2.0 Generic</dc:rights> <dc:language>eng</dc:language> <dcterms:isPartOf rdf:resource="https://kops.uni-konstanz.de/server/rdf/resource/123456789/36"/> <dc:contributor>Hinneburg, Alexander</dc:contributor> </rdf:Description> </rdf:RDF>