Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices

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SHAO, Lin, Michael BEHRISCH, Tobias SCHRECK, Ivan SIPIRAN, Bum Chul KWON, Daniel A. KEIM, 2014. Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices. Informatik 2014 - Big Data : Komplexität meistern. Stuttgart, 22. Sep 2014 - 26. Sep 2014. In: GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany. Informatik 2014 - Big Data : Komplexität meistern. Stuttgart, 22. Sep 2014 - 26. Sep 2014

@inproceedings{Shao2014Ident-30219, title={Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices}, year={2014}, booktitle={GI Workshop Big Data Visual Computing – Quantitative Perspectives for Visual Computing, September 22, 2014 Stuttgart, Germany}, author={Shao, Lin and Behrisch, Michael and Schreck, Tobias and Sipiran, Ivan and Kwon, Bum Chul 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/123456789/30219"> <dcterms:title>Identifying Locally Interesting Motifs for Exploration of Scatter Plot Matrices</dcterms:title> <dc:contributor>Behrisch, Michael</dc:contributor> <dc:creator>Shao, Lin</dc:creator> <dc:contributor>Sipiran, Ivan</dc:contributor> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-11T16:03:56Z</dc:date> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/30219"/> <dc:contributor>Keim, Daniel A.</dc:contributor> <dc:creator>Kwon, Bum Chul</dc:creator> <dc:creator>Behrisch, Michael</dc:creator> <dc:creator>Schreck, Tobias</dc:creator> <dc:creator>Sipiran, Ivan</dc:creator> <dcterms:abstract xml:lang="eng">Scatter plots are effective diagrams to visualize distributions, clusters and correlations in two-dimensional data space. For highdimensional data, scatter plot matrices can be formed to show all two-dimensional combinations of dimensions. Several previous approaches for exploration of large scatter plot spaces have focused on ranking and sorting scatter plot matrices based on global patterns. However, often local patterns are of interest for scatter plot exploration. We present a preliminary idea to explore the scatter plot space by identifying significant local patterns (also called motifs in this work). Based on certain clustering algorithms and image-based descriptors, we identify and group a set of similar local candidate motifs in a large scatter plot space.</dcterms:abstract> <dc:contributor>Kwon, Bum Chul</dc:contributor> <dc:contributor>Shao, Lin</dc:contributor> <dc:language>eng</dc:language> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-03-11T16:03:56Z</dcterms:available> <dcterms:issued>2014</dcterms:issued> <dc:creator>Keim, Daniel A.</dc:creator> <dc:contributor>Schreck, Tobias</dc:contributor> </rdf:Description> </rdf:RDF>

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