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Incremental causal discovery and visualization
RISE - Research Institutes of Sweden (2017-2019), ICT, SICS.ORCID-id: 0000-0001-8577-6745
Halmstad University, Sweden .ORCID-id: 0000-0003-3272-4145
University of Skövde, Sweden .
2019 (Engelska)Ingår i: Proceedings of the Workshop on Interactive Data Mining, WIDM 2019, Association for Computing Machinery, Inc , 2019Konferensbidrag, Publicerat paper (Refereegranskat)
Abstract [en]

Discovering causal relations from limited amounts of data can be useful for many applications. However, all causal discovery algorithms need huge amounts of data to estimate the underlying causal graph. To alleviate this gap, this paper proposes a novel visualization tool which incrementally discovers causal relations as more data becomes available. That is, we assume that stronger causal links will be detected quickly and weaker links revealed when enough data is available. In addition to causal links, the correlation between variables and the uncertainty of the strength of causal links are visualized in the same graph. The tool is illustrated on three example causal graphs, and results show that incremental discovery works and that the causal structure converges as more data becomes available. © 2019 Copyright held by the owner/author(s).

Ort, förlag, år, upplaga, sidor
Association for Computing Machinery, Inc , 2019.
Nyckelord [en]
Causal Discovery, Correlation, Incremental Visualization, Correlation methods, Data mining, Visualization, Causal graph, Causal relations, Discovery algorithm, Incremental discoveries, Novel visualizations, Data visualization
Nationell ämneskategori
Naturvetenskap
Identifikatorer
URN: urn:nbn:se:ri:diva-39672DOI: 10.1145/3304079.3310287Scopus ID: 2-s2.0-85069768142ISBN: 9781450362962 (tryckt)OAI: oai:DiVA.org:ri-39672DiVA, id: diva2:1341124
Konferens
1st Workshop on Interactive Data Mining, WIDM 2019, co-located with 12th ACM International Conference on Web Search and Data Mining, WSDM 2019, 15 February 2019
Anmärkning

Funding text 1: This research has been conducted within the “A Big Data Analytics Framework for a Smart Society" (BIDAF) project supported by the Swedish Knowledge Foundation.

Tillgänglig från: 2019-08-07 Skapad: 2019-08-07 Senast uppdaterad: 2023-11-06Bibliografiskt granskad

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Holst, AndersPashami, Sepideh

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