# A Decision-Making Tool Based on Exploratory Visualization for the Automotive Industry
## 2 Hybrid Unsupervised Exploratory Plots  
In order to extract knowledge from unlabelled datasets, many different methods could be applied. Among all of the ML methods, projection techniques are considered a viable approach for information seeking, as humans are able to recognize different features and to detect anomalies by inspecting graphs. Such patterns may become perceptible if variations are made to the spatial coordinates of the original datasets. However, an a priori choice regarding which parameters will reveal most patterns requires prior knowledge of unidentified patterns, and this is not an easy task. In order to do that, Exploratory Projection Pursuit (EPP) [8] is used for the purpose of data visualization. Contrasted with feature selection, EPP belongs to the feature extraction paradigm, as the resulting dimensions are combinations (could be linear or non-linear) of the original features in the dataset. Contrarily, clustering [9] is concerned with grouping together objects, that are similar to each other and dissimilar to objects belonging to other clusters. Therefore, patterns within the same cluster are more similar to each other than they are to a pattern belonging to a different cluster.  
Recent work [7] has proposed the independent application of EPP techniques on the one hand, and clustering them on the other. The complete results of the two of them are then combined, together with the glyph metaphor, in a novel and different way, called the Hybrid Unsupervised Exploratory Plot (HUEP). HUEPs are a general-purpose approach, in which any EPP and clustering technique could be combined to generate 3D visualizations from high-dimensional data. In Figure 1, the process to generate a HUEP is shown. These depictions are informative and intuitive, not only for data scientists, but for all the company staff (not requiring previous knowledge regarding ML). Once a proper HUEP configuration has been selected by an expert, the other company staff will only need to analyze the obtained visualization. The HUEPs can be used by 'non-expert' professionals due to their simplicity, as no further decisions or parameter-tunings are required. The proposed HUEPs are hybrid as they combine both exploratory (dimensionality reduction) techniques with clustering ones. Besides, both types of technique are unsupervised as they apply this kind of learning (no target class or value is provided to be reproduced for new data instances).  
In the original formulation of HUEPs [7], \(k\)-means [38] and Hierarchical Methods were applied as clustering techniques. Complementarily, PCA [39], MLHL [40] and CMLHL [41] were applied as EPP techniques. A wide variety of EPP techniques exists, but before this study, only the three previously-mentioned ones had been applied under the framework of HUEPs. Each EPP technique projects the data in a different way, thus for the same dataset the obtained visualizations could be very different. As happens for most of the data analysis problems, there is not a technique that always gets the best results for different datasets. Thus, depending on the analyzed data, one HUEP that uses a certain EPP method may be more suitable than another one. In keeping with this idea, we proposed in this study to extend HUEPs with other EPP techniques for a certain dataset. The main purpose is to validate that HUEPs can be extended with other EPP methods, and to prove that certain EPP methods could be more suitable than other ones when analyzing the present dataset.  
The original HUEP formulation has been extended in the present study by incorporating and validating some additional EPP techniques, namely Classical Multidimensional Scaling (CMDS), Sammon Mapping (SM) and Factor Analysis (FA)--in yellow in Figure 1. They are described in the following subsections.  
Figure 1: Process to obtain a HUEP.