# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 4 Results
### Large-Scale Real-World: Wine Dataset (N = 1599)  
We evaluated the method on the Vinho Verde dataset [5; 6], which contains 1599 samples of Portuguese red wines, each characterized by 12 physicochemical and sensory attributes measured in laboratory analysis. This dataset is widely used as a benchmark for multivariate modeling and classification tasks in enology.  
Classical visualization techniques such as PCA and t-SNE scatter plots (Figure 11) provide a global view of data structure. However, for the wine dataset, no distinct clusters or quality-based groupings are apparent; samples of differing quality are dispersed throughout both projections. Other approaches, such as parallel coordinates or glyph-based Chernoff faces, become uninformative for \(N\gg 100\) due to overplotting and loss of interpretability.  
Variable assignment:  
Quality, alcohol content, and residual sugar were assigned as avatar features (e.g., smile, face colour, nose length), as these are familiar and interpretable for non-experts (Figure 12). Four technical variables (volatile acidity, citric acid, residual sugar, sulfates) were assigned to spatial coordinates (X, Y, Z, T). All remaining technical features were omitted from the spatial mapping in this scenario.  
Figure 10: Effect of variable assignment on principal component analysis for the synthetic soft drinks dataset. (**top**) PCA on anonymous features only. (**bottom**) PCA on all features assigned to spatial coordinates or left anonymous (all except avatar features). In both cases, intuitive variables such as overall rating are not directly represented in the geometry unless they are included in the feature set, and their effect may only be visible through color. Variable abbreviations: SOU—sourness, SUG—sugar, CIT—citric acid, CO2—CO2 pressure, PRE—preservatives, COL—color intensity, PRI—price, CAF—caffeine.  
PCA loadings and spatial interpretation:  
To enable direct visual access to consumer-facing attributes, the spatial avatar visualization assigns key features to spatial axes. For comparison, principal component analysis was performed on all remaining technical features. Figure 13 presents a heatmap of PCA loadings and a corresponding scatter plot with samples coloured by quality. In the PCA projection, the principal components reflect combinations of technical features, and the spatial arrangement does not reveal distinct clusters or gradients by quality.  
Insights and sensitivity analysis:  
Assigning \"quality\" or \"alcohol\" as a spatial or PCA feature, rather than as an avatar trait, would affect the composition of principal components but reduce the immediate interpretability of these key attributes--demonstrating the practical benefit of flexible, user-driven assignment in the proposed approach.  
Figure 11: Classical projection methods for the wine dataset. (**Left**): PCA scatter plot colored by quality rating. (**Right**) t-SNE projection colored by quality. For large datasets such as this, other classical methods (e.g., parallel coordinates or glyph-based Chernoff faces) rapidly become uninterpretable due to severe overplotting and visual clutter and are, therefore, omitted.  
Figure 12: Spatial avatar visualization of the Vinho Verde wine dataset. Avatar features encode quality, alcohol content, and residual sugar; spatial coordinates reflect volatile acidity, citric acid, residual sugar, and sulfates. This mapping enables immediate identification of wines with unusual or extreme property profiles, as well as direct interpretation of both spatial position and visual traits.