# Application of Data Mining Techniques to Predict Luminance of Pavement Aggregate
## 4 Analysis of the Research Results
### Evaluation of the Effectiveness of Selected DM Techniques
This is related to a number of phenomena among which grain size and the associated alignment of aggregate grains during testing (importance = 0.7 based on Figure 8) can be mentioned. Nevertheless, it can be assumed that the occurrence of numerous local modal values of the distribution of Qd,pred results can also be attributed to the typical randomness of the mineralogical composition on the aggregate surface, resulting from the process of its crushing or the deposit occurrence. Aggregate was taken from various locations of the heap, so in all likelihood this effect could have been significant. It should be kept in mind that the large number of random effects influences the use of the final efficiency of the boosted tree model. Amphibolite aggregate has four modal values (Figure 11f), while quartzite and granite have two distinct modal values. By far the largest discrepancies between modal values were observed for gabbro aggregate (Figure 11e). Comparing two aggregates, quartzite and limestone, both modal values have almost identical Qd at the corresponding RL level. Nevertheless, by far the majority of cases are in the value range of 80 \(\div\) 85 mcd-m\({}^{-2}\).lx\({}^{-1}\) with RL45 \(\div\) 50 mcd-m\({}^{-2}\).lx\({}^{-1}\), whereas, in the case of quartzite, both modal values have almost identical values. Therefore, the use of this BT model has the advantage of being able to describe and identify such peculiarities in the value of the luminance coefficient Qd. When using traditional estimators, the variability of Qd results for quartzite and limestone aggregate would be considered insignificantly different. For additional validation, the luminance coefficient measurement results of aggregates from the northern region of Poland published by [PERSON] et al., were compared [57]. The results of the average values (solid line) are highlighted in Figure 11 with the range of the results (dashed line). The obtained results of these comparisons suggest that the presented model and results from other regions of Poland allow prediction for aggregates from other sources. It should be mentioned that the basic structure of the BT model was created on the basis of aggregates from one region, and further \"fine-tuning\" of its predictive capabilities requires successive additions to the result base. In conclusion, some critical issues should not be overlooked. As already mentioned, limestone, granite, and quartzite aggregates had high Qd and RL. In the case of limestone aggregate, it should be noted that it is characterized by a high LA and the lowest PSV (Table 3) by what, in some countries, may be a limitation in its wider use for wearing courses for heavy-traffic routes. Quartzite and granite aggregates, on the other hand, are known for their high SiO\({}_{2}\) content and thus offer low adhesion with asphalt despite the fact that they have a high and favorable PSV. Therefore, the ability to correctly predict Qd is valuable information that can be further taken into account in the optimal design of the bituminous mixture composition. Figure 11: Histogram of Qd,pred against RL: (**a**) Quartzite; (**b**) Granite (compared to results [52]); (**c**) Melaphyre; (**d**) Limestone (compared to results [52]); (**e**) Gabbro; (**f**) Amphibolite (compared to results [52]); (**g**) Basalt.