Abstract
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.
Highlights
Parallel Coordinates Plots (PCP) [1] is a well-known technique for the visualization and analysis of complex multidimensional datasets
A previous exploratory and qualitative study carried out using the first version of Immersive Parallel Coordinates Plots (IPCP) demonstrated that users were able to successfully detect patterns in high-dimensional datasets visualized as-is [2,3,4], that is, without any prior analysis or data cleaning, using the IPCP system combined with a simple naive clustering algorithm applied to cross-dimensional 3D scatter plots data [2,3,4]
This basic IPCP system allowed users to rediscover the knowledge; patterns with the data items previously found by the domain experts and, more importantly, users were able to discover new knowledge in the dataset compared to original analyses carried out by experts using a more traditional 2D PCP system [3,4]
Summary
Parallel Coordinates Plots (PCP) [1] is a well-known technique for the visualization and analysis of complex multidimensional datasets. A previous exploratory and qualitative study carried out using the first version of IPCP demonstrated that users were able to successfully detect patterns in high-dimensional datasets visualized as-is [2,3,4], that is, without any prior analysis or data cleaning, using the IPCP system (see Figure 1) combined with a simple naive clustering algorithm applied to cross-dimensional 3D scatter plots data [2,3,4] This basic IPCP system allowed users to rediscover the knowledge; patterns with the data items previously found by the domain experts and, more importantly, users were able to discover new knowledge in the dataset compared to original analyses carried out by experts using a more traditional 2D PCP system [3,4]. Instead of using hand-held controllers, this system iteration explores gaze-tracking coupled with hand-tracking supported by an additional sensor, namely the Leap Motion Sensor [9] attached to the VR head-mounted display to track and recognize the user’s hand gestures
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
