## 5 Conclusions  
Having determined the groups, detailed models for each of them (clusters) can be constructed based on, for example, generalized linear models (GLM) and factor variables. The following conclusions were formulated: \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Group 1** & **Group 2** & **Group 3** \\ \hline V\({}_{\text{m}}\) & 15 & 15 & 8 \\ ITS & 310 & 310 & 310 \\ TSR & 95 & 70 & 95 \\ W\({}_{\text{RW+2M}}\) & 95 & 70 & 95 \\ E\({}^{\star}\) (10 Hz, 5 \({}^{\circ}\)C) & 9000 & 9000 & 9000 \\ V\_1 & \(-\)0.057290 & 0.734094 & \(-\)0.600156 \\ V\_2 & 0.321659 & \(-\)0.207287 & 0.342952 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Projection coordinates of mineral–cement–emulsion (MCE) mixtures based on maximized discriminant functions. * Multidimensional analysis turned out to be an effective tool for analyzing a large data set. Owing to its algorithms, a number of generalizations can be made being the basis for creating future requirements;
* The most important element in the article was the change in the method of analyzing research results. This time, the primary issue was not comparing the averages of certain mixture cases, but searching for certain characteristic groups being a collection of similar mixture results;
* Owing to the tools such as the PCA and cluster analysis, it is possible to predict the durability of other mixtures by projecting their results into the trained space described by canonical variables;
* Variable transformation by PCA reduced the number of variables that were unrelated, which was the necessary condition for further multidimensional analysis;
* Four principal components were chosen in the dataset of samples of recycled mixtures with waste materials. The first component was related to the rheology of the samples. The second component was correlated with physical properties of the mixtures. The variation of the third component was determined through the moisture resistance parameter. The fourth component was strongly related with the density of the mixture;
* Grouping analysis using the Ward technique indicated to high probability the existence of three clusters of mix results. This was the basis for developing future recommendations;
* The first group of the recycled mixtures represented those mixtures whose behavior was elastic-brittle. The level of the stiffness modulus was high at the low level of the phase angle. The ITS was at the acceptable level relative to the requirements for MCE, as was the level of moisture resistance. This group comprised the mixtures with a high amount of RC (>40%);
* The second group represented the mixtures with low stiffness, low phase angle and low ITS. Those mixtures would not ensure the expected lifespan of pavements. The results are correlated with the low but acceptable level of moisture resistance (TSR). This group comprised the mixtures containing a high amount of RA;
* The third group represented the mixtures with high stiffness and high flexibility at 5 \({}^{\circ}\)C. This result is advantageous from the perspective of low-temperature-induced cracking reduction. As the mixtures contained high amounts of bitumen added with dusts and a high RAP content (>50%), the mixtures will be resistant to moisture damage, as demonstrated in this study;
* Discriminant analysis revealed that rheological and structural properties of the recycled mixtures had the highest discriminant power and that moisture resistance parameter was the least helpful;
* The recycled mixtures recommended for use were those with mineral dusts and high amount of bitumen, and those with a high RAP content. The analysis revealed that large amounts of RA material (more than 40%) in MCAS composition should be carefully considered with caution;
* Based on the results of the experiment, MCAS mixes with the use of some waste materials in an appropriate amount allow obtaining mixes for road base layer with parameters better than the traditional recycled mixture performed in MCE technology.