## 4 Evaluation and Results
### Machine Learning Applied to Data Analysis
#### 4.2.2 Clustering Analysis: Natural Aggrupation  
Clustering analysis is used to find out the natural aggrupation of STPs, since the whole taxonomy analysis from this chapter is based on the expert labelling in three STP types and observing and analysing analogies or differences between these groups. Through clustering, unobserved patterns can be revealed. Many combinations of core features were used in this extensive clustering analysis, without considering, as in Section 4.2.1, Fe19. Innovative profile 2 and Fe14. Employment R&D due to their excessive missing data. The developed dashboard allows the use of different combinations of features and a different number of clusters (K). The silhouette score is obtained for each combination, which shows the efficiency of a clustering technique and aims to interpret and validate the cluster analysis [19]. In the silhouette score (s), the interpretation is:  
* 0.71-1.0: well-defined structure;
* 0.51-0.70: \"reasonably\" well-defined structure;
* 0.26-0.50: weak structure, may be artificial--it is suggested that we try other methods;
* \(<\)0.25: no structure has been discovered in the data. Different combinations of features and number of clusters were applied and the evolution of the silhouette score was analysed. A sample of the most relevant combinations of features is presented in Table 4, where values of s over 0.51 are highlighted. Fe15 and Fe19 are not considered in any combination, as missing data accounts for over 16% for these features. The most interesting approaches in this clustering analysis for the aim of this study, both of them based on impact, are as follows:  
* Standard economic impact, where the typically considered features are Fe05. Turnover and Fe06. Employment (#4 in Table 4);
* Innovation and entrepreneurship focus impact, where the core features considered are Fe18. Innovative profile 1 and Fe13. Incubation ratio (#5 in Table 4). Figure 7a,b shows the results of the clustering analysis for both impact considerations. A description of the clusters formed is also included. The standard economic impact (Figure 7a) delivers a maximum when K = 2. This clustering shows two groups, one being the larger STPs, that are three TPs. The next clustering analysis when K = 3 presents a remarkable, well-defined structure as indicated by s = 0.72. This delivers more information and, therefore, is the one represented in Figure 7a. This result suggests that the following three clusters can be defined considering the economic impact: the cluster 2 TP type with medium turnover and medium employment rates; the cluster 1 big TPs with higher rates; and cluster 0, a mixture of TPs, SPs, and HPs in the third, and numerous clusters with moderate rates for turnover and employment. The clustering results for the impact in terms of innovation and entrepreneurship (Figure 7b) present a maximum when K = 4, but the fourth cluster was formed only by one TP with a high incubation ratio (Fe13), an outlier, presenting a value over 1. Apparently, this TP incubates more young companies than the companies belonging to it. Therefore, clustering when K = 3 for this case gives more information and presents a very similar and acceptable s = 0.56, which means a reasonably well-defined structure. These results suggest that the following three valid clusters can be defined considering the innovation and entrepreneurship impact: the cluster 2 SP type with remarkable innovation and entrepreneurship ratios, which contains 60% of the SPs; the cluster 0 TP type with low ratios, which contains 76% of the TPs; and cluster 1, a cluster with medium ratios and a mix of three SPs and four TPs. In line with the decision tree analysis (Section 4.2.1), the clustering analysis conducted shows that some TPs tend to cluster according to their features with SPs and vice versa. The centroids of each cluster represent the typical features and can indicate the distance of an STP from the prototype. This distance can support decision making in the management of an STP.