# Visualization of a Multidimensional Point Cloud as a 3D Swarm of Avatars
## 4 Results
### Medium-Scale Illustration: Synthetic Soft Drinks Dataset (N = 100)  
The synthetic soft drinks dataset comprises 100 fictional beverages generated to simulate market diversity with three main product groups (classic, premium, and extreme). Each beverage is described by intuitive sensory attributes (sweetness, fizziness, overall rating, and color intensity) and technical features (price and caffeine). The group_numeric variable encodes the product segment, used only for color coding; each sample is also labeled by name. To provide context for the effectiveness of our approach, Figure 8 shows the synthetic drinks dataset visualized using four classical techniques: PCA scatter plot, t-SNE projection, parallel coordinates, and a Chernoff faces grid for selected products. PCA and t-SNE projections highlight clear clustering by product type, with the principal components and t-SNE axes separating the main beverage groups. However, interpreting which specific features drive the separation or identifying outliers based on concrete sensory traits remains challenging. Parallel coordinates enable a detailed comparison of feature values but quickly become cluttered at this scale. Chernoff faces communicate some intuitive traits yet are difficult to interpret en masse and do not encode structural relationships between products. Figure 8: Comparison of classical visualization methods for the synthetic soft drinks dataset. (**Top left**): PCA scatter plot; (**Top right**): t-SNE projection; (**Bottom left**): parallel coordinates plot; (**Bottom right**): Chernoff faces grid (6 \(\times\) 4) for a subset of beverages, colored by group. While PCA and t-SNE reveal some group structure, and parallel coordinates enable visualization of feature distributions, Chernoff faces offer a glimpse into sensory profiles of selected products. However, none of these methods provides a direct, interpretable synthesis of both product similarity and profile, as does the spatial avatar approach. Variable assignment:  
Intuitive features (sweetness, fizziness, overall rating, color intensity) were mapped to avatar visual traits (e.g., smile, nose length, skin color, hair color). Technical features (price, caffeine) were used for spatial coordinates (X and Y axes). The remaining attributes--sourness, sugar, citric acid, CO2 pressure, preservatives, and color intensity--were included as anonymous variables for PCA-based analysis. This assignment is reflected in the Figure 9.  
PCA loadings and spatial interpretation:  
Principal component analysis was performed under two scenarios: (top) only on anonymous features (not mapped to avatars or spatial coordinates) and (bottom) on all features assigned to spatial coordinates or left anonymous. Figure 10 presents heatmaps of PCA loadings and corresponding scatter plots for both cases. Notably, in both PCA scenarios, clear clusters emerge corresponding to beverage groups, as principal components are determined by mixtures of compositional variables (e.g., sourness, sugar, citric acid, preservatives). However, while structural groupings are evident, the interpretation of which sensory or technical attributes drive the group separation and direct identification of outliers remains less intuitive than in the spatial avatar visualization, where both group membership and profile details are visible at a glance. Insights and sensitivity analysis:  
This example demonstrates that, although dimensionality reduction techniques like PCA and t-SNE can recover the underlying group structure when relevant features dominate the variance, only the spatial avatar approach allows for the immediate interpretation of which specific sensory traits differentiate products, providing direct, profile-based insight. Switching a feature, such as \"sweetness\" or \"fizziness\", from an avatar trait to an anonymous (PCA) feature alters the PCA scatter plot and principal component structure but at the expense of immediate interpretability in the avatar view.