# A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
## 4 Results
### Cyclone Results  
Results shown in this section include visualizations of all the data samples in the Cyclone dataset. Each one of them is depicted by adding the following extra information (related to the machine status) through the glyph metaphor:  
* x (red x): suction circuit is blocked;
* + (black +): vacuum malfunctioning;
* - (blue point): no problem reported (i.e., cyclone properly working). As for the previous dataset, the best EPP visualizations are shown in Figure 7. In this case, the selected ones are the CMLHL (Figure 7a) and CMDS (Figure 7b) projections. It can be observed that none of the EPP techniques generate a visualization that clearly separates the data samples associated to anomalies. These projections are significantly enhanced when combined with the clustering results in the HUEPs, as shown in Figure 8.  
Figure 7: 3D visualizations generated by EPP methods for the cyclone dataset. (**a**) CMLHL 3D visualization. (**b**) CMDS–Seuclidean 3D visualization. Figure 8: HUEP visualizations of the cyclone dataset. EPP+hierarchical. (**a**) HUEP: PCA+hierarchical clustering (\(k\) = 6, Cityblock, and weighted). (**b**) HUEP: MLHL+hierarchical clustering (\(k\) = 6, Cityblock, and weighted). (**c**) HUEP: CMLHL+hierarchical clustering (\(k\) = 6, Cityblock, and weighted). (**d**) HUEP: CMDS–Seuclidean+hierarchical clustering (\(k\) = 6, Cityblock, and weighted). (**e**) HUEP: SM+hierarchical clustering (\(k\) = 6, Cityblock, and weighted). (**f**) HUEP: FA+hierarchical clustering (\(k\) = 6, Cityblock, and weighted). As has been shown for the previous dataset, it is worth mentioning that CMDS generates the best HUEPs for the cyclone dataset. The best result is obtained with CMDS with Seuclidean + hierarchical clustering (\(k\) = 6, Cityblock, and weighted), shown in Figures 8d and 9c. In these visualizations, the critical failure (suction circuit is blocked) samples (red x) are isolated in a unique group, as are those samples associated to the other anomaly (black +), which are also depicted in a separated group. The HUEPs generated for this dataset can clearly visualize the critical failure in a separate group. This is done thanks to the combination with clustering results; the samples associated to the main failure are grouped in the same cluster, clearly visualizing them in a separate group. Although the samples from the other anomaly (vacuum malfunctioning) are clustered with many \"normal\" samples, the CMDS projection visualizes them in a separate way. Once again, CMDS is the EPP method generating the best HUEP, and additional results obtained with this method are shown in Figure 9. The main purpose of this figure is to compare the visualizations generated when clustering parameters are varied. Figure 9 shows the results obtained by combining CMDS-Seuclidean with \(k\)-means and hierarchical clustering. The hierarchical clustering method can separate into cluster #1 the critical-failure samples (red x), but \(k\)-means groups these data together with \"normal\" samples. It can thus be said that, as in  
Figure 9: HUEP visualizations generated by CMDS-Seuclidean and different clustering parameters for the cyclone dataset. (**a**) HUEP: CMDS-Seuclidean+\(k\)-means (\(k\) = 3 and Correlation). (**b**) HUEP: CMDS-Seuclidean+\(k\)-means (\(k\) = 6 and Correlation). (**c**) HUEP: CMDS-Seuclidean+hierarchical clustering (\(k\) = 6, Cityblock, and weighted). the case of the previous dataset, hierarchical clustering leads to better visualization results for the cyclone dataset. In general terms, the HUEPs generated by agglomerative (hierarchical) clustering are more informative than those generated by \(k\)-means for the two datasets under analysis. This is consistent with general heuristics [52], as agglomerative clustering is more appropriate if groups are expected to be different sizes. Opposingly, \(k\)-means is the best option when the expected groups have approximately similar sizes.