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Scalable Cluster Analysis of Spatial Events
University of Bonn and Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Germany.
University of Bonn and Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Germany.
Linköping University, Department of Science and Technology, Media and Information Technology. Linköping University, The Institute of Technology.ORCID iD: 0000-0003-4761-8601
University of Bonn and Fraunhofer Institute for Intelligent Analysis and Information Systems (IAIS), Germany.
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2012 (English)In: EuroVA 2012: International Workshop on Visual Analytics / [ed] K. Matkovic and G. Santucci, Eurographics - European Association for Computer Graphics, 2012, 19-23 p.Conference paper, Published paper (Refereed)
Abstract [en]

Clustering 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.

Place, publisher, year, edition, pages
Eurographics - European Association for Computer Graphics, 2012. 19-23 p.
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:liu:diva-78862DOI: 10.2312/PE/EuroVAST/EuroVA12/019-023OAI: oai:DiVA.org:liu-78862DiVA: diva2:536279
Conference
International Eurovis workshop on Visual Analytics, EuroVA
Available from: 2012-06-21 Created: 2012-06-21 Last updated: 2015-06-02

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Vrotsou, Katerina

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