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Eliciting structure in data
RISE SICS, Sweden.
CAISR, Halmstad, Sweden.
RISE SICS, Sweden.
CAISR, Halmstad, Sweden.
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2019 (English)In: CEUR Workshop Proceedings / [ed] Christoph Trattner, Denis Parra, Nathalie Riche, CEUR-WS , 2019, Vol. 2327Conference 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. 

Place, publisher, year, edition, pages
CEUR-WS , 2019. Vol. 2327
Series
CEUR Workshop Proceedings, ISSN 1613-0073 ; 2327
Keywords [en]
Anomaly detection, Causal inference, Clustering, Distributed analytics, Higher-order structure, Information visualization, Information systems, User interfaces, Causal inferences, Data acquisition
National Category
Computer Sciences Human Computer Interaction
Research subject
Skövde Artificial Intelligence Lab (SAIL)
Identifiers
URN: urn:nbn:se:his:diva-16748Scopus ID: 2-s2.0-85063227224OAI: oai:DiVA.org:his-16748DiVA, id: diva2:1302778
Conference
2019 Joint ACM IUI Workshops, ACMIUI-WS 2019, Los Angeles, United States, 20 March 2019
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-09-30Bibliographically approved

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Falkman, GöranKarlsson, AlexanderSaid, AlanBae, Juhee

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