Scalable Cluster Analysis of Spatial Events

dc.contributor.authorPeca, Iulianen_US
dc.contributor.authorFuchs, Georgen_US
dc.contributor.authorVrotsou, Katerinaen_US
dc.contributor.authorAndrienko, Nataliaen_US
dc.contributor.authorAndrienko, Gennadyen_US
dc.contributor.editorKresimir Matkovic and Giuseppe Santuccien_US
dc.date.accessioned2013-11-08T10:21:27Z
dc.date.available2013-11-08T10:21:27Z
dc.date.issued2012en_US
dc.description.abstractClustering of massive data is an important analysis tool but also challenging since the data often does not fit in RAM. Many clustering algorithms are thus severely memory-bound. This paper proposes a deterministic density clustering algorithm based on DBSCAN that allows to discover arbitrary shaped clusters of spatio-temporal events that (1) achieves scalability to very large datasets not fitting in RAM and (2) exhibits significant execution time improvements for processing the full dataset compared to plain DBSCAN. The proposed algorithm's integration with interactive visualization methods allows for visual inspection of clustering results in the context of the analysis task; several alternatives are discussed by means of an application example about traffic data analysis.en_US
dc.description.seriesinformationEuroVA 2012: International Workshop on Visual Analyticsen_US
dc.identifier.isbn978-3-905673-89-0en_US
dc.identifier.urihttps://doi.org/10.2312/PE/EuroVAST/EuroVA12/019-023en_US
dc.publisherThe Eurographics Associationen_US
dc.subjectCategories and Subject Descriptors (according to ACM CCS): I.5.3 [Pattern Recognition]: Clustering-Algorithmsen_US
dc.titleScalable Cluster Analysis of Spatial Eventsen_US
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