## 3 Calculation
The aim is to demonstrate their statistically significant influence on the classification of the object to the correct material and to exclude those attributes that cause noise when calculating Euclidean distance. For selecting appropriate criteria, the same criteria were established for objects created from sequences of length 500 values. Table 4 presents calculated criteria values for objects created from sequences of 1500 values. Where the criterion that the null hypothesis was not rejected for the ANOVA test was not met, the other criteria were not calculated. The established criteria have met the following attributes: _Sum_of_abs_, _Count Peaks_10_, _Count Peaks_15_, _Variab_. In addition to these attributes, the Fluctuation attribute has been retained in the object vector. The following graphs (Figures 4 and 5) display a box plot for objects and sequences of 1500 values for each selected attribute. Figure 3: Cluster dendrogram of rock objects for sequence 1500 and all attributes. It is clear from the graphs that distinguishing between concrete and andesite materials is easy. However, this is insufficient for the correct distinction between granite and limestone. On the other hand, it is possible that using all of the selected attributes will make it possible to classify the objects of individual rocks correctly. A cluster dendrogram (Figure 6) was created from objects of individual materials for five attributes. It has unambiguously Euclidean distances separated into clusters of objects of concrete (c) in Cluster C1 and andesite (a) in Cluster C2. The group of limestone objects (l) is also classified into a single cluster, C3. The right cluster of the third level--C4--looks the worst, with objects of granite (g) and limestone (l). \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \multirow{2}{*}{**Attribute**} & \multicolumn{4}{c}{**\(p\)-Value**} & \multicolumn{4}{c}{**Tukey Pairwise \(p\)\_adj**} \\ \cline{2-10}  & **ANOVA** & **Shapiro–Wilk Test** & **Bartlett’s Test** & **c-a** & **l-a** & **g-a** & **l-c** & **g-c** & **g-l** \\ \hline \(Sum\_of\_abs\) & \textless{}2 \(\times\) 10\({}^{-16}\) & 0.199 & 0.405 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.007 \\ \(Sum\_o\) & 0.871 & - & - & - & - & - & - & - & - \\ \(Fluctuation\) & \(4.2\times 10^{-15}\) & 0.408 & 0.800 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.72 \\ \(Count\_Pasks\_10\) & \textless{}2 \(\times\) 10\({}^{-16}\) & 0.360 & 0.279 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.014 \\ \(Count\_Pasks\_15\) & \textless{}2 \(\times\) 10\({}^{-16}\) & 0.534 & 0.427 & 0.000 & 0.000 & 0.000 & 0.000 & 0.000 & 0.021 \\ \(Count\_Pasks\_20\) & \textless{}2 \(\times\) 10\({}^{-16}\) & 0.839 & 0.072 & 0.000 & 0.000 & 0.000 & 0.000 & 0.246 \\ \(Variable\) & \textless{}2 \(\times\) 10\({}^{-16}\) & 0.386 & 0.054 & 0.000 & 0.000 & 0.000 & 0.000 & 0.007 \\ \(Skew\) & 0.375 & - & - & - & - & - & - & - & - \\ \(Kurt\) & 0.503 & - & - & - & - & - & - & - & - \\ \hline \end{tabular}
\end{table} 
Table 4: Results of ANOVA and pairwise comparisons for objects created from a sequence of length 1500 values. Figure 4: Box plot for rock object attributes of a sequence of 1500 values; (**a**) _Sum_of_abs_; (**b**) _Var_. Evaluation of findings: The vibration signal attributes used for a specified sequence length are unsuitable for classification. The following procedure is chosen based on looking at the dendrogram (Figure 6) and examining the classification of objects into clusters. It becomes clear that Cluster C3, grouping limestone objects (l), contains at least one of three consecutive objects. That is, from the objects l_2, l_3, l_4, it contains objects l_2 and l_4; of the objects l_3, l_4, l_5, it contains object l_4; of the objects l_4, l_5, l_6, it contains l_4 and l_6; of the objects l_5, l_6, l_7, it contains objects l_6 and l_7; of the objects l_6, l_7, l_8, it contains l_6 and l_7; of the objects l_7, l_8, l_9, it contains l_7 and l_9; of the objects l_8, l_9, l_10, it contains l_9.