## 8 Conclusions  
The previously executed formative studies with a small group of domain experts [2; 3; 4] has shown that it is possible to design an effective and immersive version of the ParallelCoordinates Plots that can be used to identify patterns in a complex and multivariate dataset. Hence, an updated version of the IPCP was proposed in this paper, which is enhanced with new features and functionality.  
A new data-analytics toolkit has been integrated with IPCP and offers three additional functionalities. These are the automated ordering of the axes based on a correlation indicator property (see Figure 6), the ordering of the axes based on the importance with respect to a chosen cluster labels assignment, and a method for automated clustering. All of these features assist and prompt the user for pattern identification in multidimensional datasets.  
The hardware used for the interaction between the system and its user was based on gestural input coupled with gaze tracking discussed in detail in [PERSON] et al. [4].  
Furthermore, this research opens up new avenues for future investigations. For example, we could enhance our data-analytics toolkit with a number of new functionalities from XAI methods [34, 35, 36]. As suggested in [PERSON] et al. [4], such an extension would permit the user to answer more complex queries. For example, the user could ask: \"Why were these particular data points excluded from the identified clusters?\" (see Figure 2).  
Another interesting avenue for further research is to use the voice-operated interface in which the user's speech could be coupled with gestural input to operate the system. [4, 38]. Here, we can build a bespoke, immersive environment enhanced with the knowledge database containing complex domain knowledge [38]. As the context in which the user operates is finite and constrained by the particular use case as well as by possible interaction techniques, such a solution can also be applied in visual analytics, including IPCP [38]. A similar approach for a voice-operated aeroengine health monitoring system realized with the help of a VR environment was proposed in [PERSON] et al. [38].  
Conceptualization, S.K.T., T.K. and S.B.; methodology, P.O.K. and J.T.; software, S.K.T., S.B., L.S. and P.S.; validation, T.K., J.T. and G.J.N.; formal analysis, S.B. and L.S.; investigation, S.K.T., S.B. and L.S.; resources, T.K., P.O.K., J.T. and G.J.N.; data curation, T.K.; writing--original draft preparation, S.B., L.S. and S.K.T.; writing--review and editing, P.O.K., T.K., J.T. and G.J.N.; visualization, S.B. and S.K.T.; supervision, P.O.K., J.T. and G.J.N.; project administration, P.O.K., J.T. and G.J.N.; funding acquisition, P.O.K., J.T. and G.J.N. All authors have read and agreed to the published version of the manuscript.  
The work of [PERSON] was supported by the Cambridge European and Trinity Hall and the EPSRC (1788814) scholarships. The work of [PERSON] and [PERSON] was funded from the PACMEL project funded by the National Science Centre, Poland under the CHIST-ERA programme (NCN 2018/27/Z/ST6/03392).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.  
The following abbreviations are used in this manuscript:  
AI Artificial Intelligence  
DBSCAN Density-Based Spatial Clustering of Applications with Noise  
DS1 Dataset 1 (Pareto front data; 54 data items with 29 dimension each)  
DS2 Dataset 2 (S-duct design optimization study; 166 data items with 39 dimensions each)  
HMD Head-Mounted Display  
IPCP Immersive Parallel Coordinates Plots  
OPTICS Identify the Clustering Structure  
PCA Principal Component Analysis  
PCP Parallel Coordinates Plots  
SDK Software Development Kit  
SuMC Subspace Memory Clustering  
VR Virtual Reality  
XAI Explainable Artificial Intelligence