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Virtual reality-based parallel coordinates plots enhanced with explainable ai and data-science analytics for decision-making processes

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cam.issuedOnline2021-12-30
dc.contributor.authorBobek, S
dc.contributor.authorTadeja, SK
dc.contributor.authorStruski, Ł
dc.contributor.authorStachura, P
dc.contributor.authorKipouros, T
dc.contributor.authorTabor, J
dc.contributor.authorNalepa, GJ
dc.contributor.authorKristensson, PO
dc.contributor.orcidTadeja, Slawomir [0000-0003-0455-4062]
dc.contributor.orcidKipouros, Timoleon [0000-0003-3392-283X]
dc.contributor.orcidKristensson, Per Ola [0000-0002-7139-871X]
dc.date.accessioned2022-01-10T12:45:04Z
dc.date.available2022-01-10T12:45:04Z
dc.date.issued2022
dc.date.updated2022-01-10T12:45:03Z
dc.description.abstract<jats:p>We present a refinement of the Immersive Parallel Coordinates Plots (IPCP) system for Virtual Reality (VR). The evolved system provides data-science analytics built around a well-known method for visualization of multidimensional datasets in VR. The data-science analytics enhancements consist of importance analysis and a number of clustering algorithms including a novel SuMC (Subspace Memory Clustering) solution. These analytical methods were applied to both the main visualizations and supporting cross-dimensional scatter plots. They automate part of the analytical work that in the previous version of IPCP had to be done by an expert. We test the refined system with two sample datasets that represent the optimum solutions of two different multi-objective optimization studies in turbomachinery. The first one describes 54 data items with 29 dimensions (DS1), and the second 166 data items with 39 dimensions (DS2). We include the details of these methods as well as the reasoning behind selecting some methods over others.</jats:p>
dc.identifier.doi10.17863/CAM.79927
dc.identifier.eissn2076-3417
dc.identifier.issn2076-3417
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/332477
dc.languageen
dc.language.isoeng
dc.publisherMDPI AG
dc.publisher.urlhttp://dx.doi.org/10.3390/app12010331
dc.subjectvirtual reality
dc.subjectdecision-making
dc.subjectexplainable AI
dc.subjectvisualization
dc.subjectvisual analytics
dc.subjectimmersive analytics
dc.titleVirtual reality-based parallel coordinates plots enhanced with explainable ai and data-science analytics for decision-making processes
dc.typeArticle
dcterms.dateAccepted2021-12-25
prism.issueIdentifier1
prism.publicationNameApplied Sciences (Switzerland)
prism.volume12
pubs.funder-project-idEPSRC (1788814)
rioxxterms.licenseref.urihttps://creativecommons.org/licenses/by/4.0/
rioxxterms.versionVoR
rioxxterms.versionofrecord10.3390/app12010331

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