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

Published version
Peer-reviewed

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Authors

Bobek, S 
Tadeja, SK 
Struski, Ł 
Stachura, P 

Abstract

jats:pWe 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>

Description

Keywords

virtual reality, decision-making, explainable AI, visualization, visual analytics, immersive analytics

Journal Title

Applied Sciences (Switzerland)

Conference Name

Journal ISSN

2076-3417
2076-3417

Volume Title

12

Publisher

MDPI AG
Sponsorship
EPSRC (1788814)