Integrating Guided Clustering in Visual Analytics to Support Domain Expert Reasoning Processes

dc.contributor.authorMathisen, Andreasen_US
dc.contributor.authorNielsen, Matthiasen_US
dc.contributor.authorGrønbæk, Kajen_US
dc.contributor.editorAnna Puig Puig and Tobias Isenbergen_US
dc.date.accessioned2017-06-12T05:17:53Z
dc.date.available2017-06-12T05:17:53Z
dc.date.issued2017
dc.description.abstractRecent research shows promise in combining Information Visualization (IV) and Machine Learning (ML) to assist data analysis performed by domain experts. However, this approach presents non-trivial challenges, in particular when the goal is to incorporate knowledge provided by the domain expert in underlying ML algorithms. To address these challenges, we present an analytical process and a visual analytics tool that uses visual queries to capture examples from the domain experts' existing reasoning process which will guide the subsequent clustering. Our work is motivated by a collaboration with personnel at the Danish Business Authority, who are interested in two types of insights: (1) On which data dimensions is a selected subset of companies different from the remaining companies? (2) Which other companies lie within the same multi-dimensional subspace? The poster will illustrate a real analysis scenario, where the presented analytic process allows auditors to use their knowledge of identified "suspicious" companies to kick-start the analysis for others.en_US
dc.description.sectionheadersPosters
dc.description.seriesinformationEuroVis 2017 - Posters
dc.identifier.doi10.2312/eurp.20171164
dc.identifier.isbn978-3-03868-044-4
dc.identifier.pages41-43
dc.identifier.urihttps://doi.org/10.2312/eurp.20171164
dc.identifier.urihttps://diglib.eg.org:443/handle/10.2312/eurp20171164
dc.publisherThe Eurographics Associationen_US
dc.titleIntegrating Guided Clustering in Visual Analytics to Support Domain Expert Reasoning Processesen_US
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