Unifying change : Towards a framework for detecting the unexpected

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ADÄ, Iris, Michael BERTHOLD, 2011. Unifying change : Towards a framework for detecting the unexpected. 2011 IEEE International Conference on Data Mining Workshops (ICDMW). Vancouver, BC, Canada, 11. Dez 2011 - 11. Dez 2011. In: 2011 IEEE 11th International Conference on Data Mining Workshops. 2011 IEEE International Conference on Data Mining Workshops (ICDMW). Vancouver, BC, Canada, 11. Dez 2011 - 11. Dez 2011. IEEE, pp. 555-559. ISBN 978-1-4673-0005-6

@inproceedings{Ada2011-12Unify-19351, title={Unifying change : Towards a framework for detecting the unexpected}, year={2011}, doi={10.1109/ICDMW.2011.173}, isbn={978-1-4673-0005-6}, publisher={IEEE}, booktitle={2011 IEEE 11th International Conference on Data Mining Workshops}, pages={555--559}, author={Adä, Iris and Berthold, Michael} }

<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/123456789/19351"> <dcterms:issued>2011-12</dcterms:issued> <dcterms:available rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-05-23T09:50:58Z</dcterms:available> <dc:contributor>Berthold, Michael</dc:contributor> <dc:language>eng</dc:language> <dc:rights>deposit-license</dc:rights> <dcterms:title>Unifying change : Towards a framework for detecting the unexpected</dcterms:title> <dcterms:rights rdf:resource="http://nbn-resolving.org/urn:nbn:de:bsz:352-20140905103605204-4002607-1"/> <dcterms:abstract xml:lang="eng">An interesting challenge in data stream mining is the detection of events where events are generally defined as anything previously unknown in the data. Therefore outliers, but also model changes or drifts, can be considered as possible events. Various methods for event detection have been proposed for different types of events. In this paper, we describe a more general framework for event detection. The framework enables generic types of time slots and streaming progress through time to be incorporated. It allows measures of similarity to included between those slots, either based directly on the data, or an abstraction, e.g. a model built on the data. We demonstrate that a large number of existing algorithms fit nicely into this framework by choosing appropriate time slots, progress mechanisms, and similarity functions.</dcterms:abstract> <dc:creator>Berthold, Michael</dc:creator> <bibo:uri rdf:resource="http://kops.uni-konstanz.de/handle/123456789/19351"/> <dc:contributor>Adä, Iris</dc:contributor> <dcterms:bibliographicCitation>Publ. in: 11th IEEE International Conference on Data Mining Workshops : proceedings ; Vancouver, Canada, 11 December 2011 / Myra Spiliopoulou ... (eds.). - Los Alamitos, Calif. : IEEE Computer Society, 2011. - S. 555-559. - ISBN 978-1-4673-0005-6</dcterms:bibliographicCitation> <dc:creator>Adä, Iris</dc:creator> <dc:date rdf:datatype="http://www.w3.org/2001/XMLSchema#dateTime">2012-05-23T09:50:58Z</dc:date> </rdf:Description> </rdf:RDF>

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