Analysis of Patient Groups and Immunization Results Based on Subspace Clustering

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HUND, Michael, Werner STURM, Tobias SCHRECK, Torsten ULLRICH, Daniel KEIM, Ljiljana MAJNARIC, Abdreas HOLZINGER, 2015. Analysis of Patient Groups and Immunization Results Based on Subspace Clustering. 8th International Conference, BIH. London, 30. Aug 2015 - 2. Sep 2015. In: GUO, Yike, ed. and others. Brain Informatics and Health : 8th International Conference, BIH 2015, London, UK, August 30 - September 2, 2015; Proceedings. 8th International Conference, BIH. London, 30. Aug 2015 - 2. Sep 2015. Cham [u.a.]:Springer, pp. 358-368. ISSN 0302-9743. eISSN 1611-3349. ISBN 978-3-319-23343-7

@inproceedings{Hund2015Analy-32002, title={Analysis of Patient Groups and Immunization Results Based on Subspace Clustering}, year={2015}, doi={10.1007/978-3-319-23344-4_35}, number={9250}, isbn={978-3-319-23343-7}, issn={0302-9743}, address={Cham [u.a.]}, publisher={Springer}, series={Lecture Notes in Computer Science / Lecture notes in artificial intelligence}, booktitle={Brain Informatics and Health : 8th International Conference, BIH 2015, London, UK, August 30 - September 2, 2015; Proceedings}, pages={358--368}, editor={Guo, Yike}, author={Hund, Michael and Sturm, Werner and Schreck, Tobias and Ullrich, Torsten and Keim, Daniel and Majnaric, Ljiljana and Holzinger, Abdreas} }

<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/123456789/32002"> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/32002"/> <dc:creator>Sturm, Werner</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-10-28T09:48:01Z</dc:date> <dc:contributor>Sturm, Werner</dc:contributor> <dc:creator>Holzinger, Abdreas</dc:creator> <dc:contributor>Ullrich, Torsten</dc:contributor> <dc:creator>Keim, Daniel</dc:creator> <dc:creator>Schreck, Tobias</dc:creator> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2015-10-28T09:48:01Z</dcterms:available> <dc:creator>Majnaric, Ljiljana</dc:creator> <dcterms:title>Analysis of Patient Groups and Immunization Results Based on Subspace Clustering</dcterms:title> <dc:language>eng</dc:language> <dc:contributor>Holzinger, Abdreas</dc:contributor> <dc:creator>Ullrich, Torsten</dc:creator> <dcterms:abstract xml:lang="eng">Biomedical experts are increasingly confronted with what is often called Big Data, an important subclass of high-dimensional data. High-dimensional data analysis can be helpful in finding relationships between records and dimensions. However, due to data complexity, experts are decreasingly capable of dealing with increasingly complex data. Mapping higher dimensional data to a smaller number of relevant dimensions is a big challenge due to the curse of dimensionality. Irrelevant, redundant, and conflicting dimensions affect the effectiveness and efficiency of analysis. Furthermore, the possible mappings from high- to low-dimensional spaces are ambiguous. For example, the similarity between patients may change by considering different combinations of relevant dimensions (subspaces). We show the potential of subspace analysis for the interpretation of high-dimensional medical data. Specifically, we analyze relationships between patients, sets of patient attributes, and outcomes of a vaccination treatment by means of a subspace clustering approach. We present an analysis workflow and discuss future directions for high-dimensional (medical) data analysis and visual exploration.</dcterms:abstract> <dc:contributor>Hund, Michael</dc:contributor> <dc:contributor>Majnaric, Ljiljana</dc:contributor> <dc:creator>Hund, Michael</dc:creator> <dc:contributor>Schreck, Tobias</dc:contributor> <dc:contributor>Keim, Daniel</dc:contributor> <dcterms:issued>2015</dcterms:issued> </rdf:Description> </rdf:RDF>

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