Virtual reality-based parallel coordinates plots enhanced with explainable ai and data-science analytics for decision-making processes
Authors
Bobek, S
Struski, Ł
Stachura, P
Tabor, J
Nalepa, GJ
Publication Date
2022-01Journal Title
Applied Sciences (Switzerland)
ISSN
1454-5101
Publisher
MDPI AG
Volume
12
Issue
1
Language
en
Type
Article
This Version
VoR
Metadata
Show full item recordCitation
Bobek, S., Tadeja, S., Struski, Ł., Stachura, P., Kipouros, T., Tabor, J., Nalepa, G., & et al. (2022). Virtual reality-based parallel coordinates plots enhanced with explainable ai and data-science analytics for decision-making processes. Applied Sciences (Switzerland), 12 (1) https://doi.org/10.3390/app12010331
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>
Keywords
virtual reality, decision-making, explainable AI, visualization, visual analytics, immersive analytics
Sponsorship
EPSRC (1788814)
Identifiers
External DOI: https://doi.org/10.3390/app12010331
This record's URL: https://www.repository.cam.ac.uk/handle/1810/332477
Rights
Licence:
https://creativecommons.org/licenses/by/4.0/
Statistics
Total file downloads (since January 2020). For more information on metrics see the
IRUS guide.