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Eliciting structure in data
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-8577-6745
CAISR, Sweden.
RISE - Research Institutes of Sweden, ICT, SICS.ORCID iD: 0000-0001-9244-4546
CAISR, Sweden.
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2019 (English)In: CEUR Workshop Proceedings, 2019Conference paper, Published paper (Refereed)
Abstract [en]

This paper demonstrates how to explore and visualize different types of structure in data, including clusters, anomalies, causal relations, and higher order relations. The methods are developed with the goal of being as automatic as possible and applicable to massive, streaming, and distributed data. Finally, a decentralized learning scheme is discussed, enabling finding structure in the data without collecting the data centrally. © 2019 Copyright held for the individual papers by the papers’ authors. Copying permitted for private and academic purposes. This volume is published and copyrighted by its editors.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-38261Scopus ID: 2-s2.0-85063227224OAI: oai:DiVA.org:ri-38261DiVA, id: diva2:1301640
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, 20 March 2019
Available from: 2019-04-02 Created: 2019-04-02 Last updated: 2019-04-02Bibliographically approved

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Scopushttp://ceur-ws.org/Vol-2327/IUI19WS-UIBK-4.pdf

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Holst, AndersGörnerup, OlofAl-Shishtawy, AhmadGirdzijauskas, Sarunas

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