## 3 Calculation
In Table 2, for the first object from each rock, the _Fluctuation_ and _Count_peaks_ attributes have higher values than other objects of the same material. This fact results from the procedure for recording a vibration signal immediately after starting drilling. The drilling tool has touched the surface, and the initial vibrations that have been recorded have high attribute values and do not match the values of the attribute from the recording at the time when the drilling tool was already in a working position inside the material. Such objects cannot be included in the analysis. Objects for which the _Start_order_ is larger than 500 will be included in the analysis, corresponding to a rise time of 1/36 of a second. For ten randomly selected objects from each material and attribute vector (_Sum_of_abs_, _Sum_o_, _Fluctuation_, _Count peaks_10_, _Count peaks_15_, _Count peaks_20_, _Variab_, _Skew_, _Kurt_), a dendrogram was created (Figure 1) to verify that the Euclidean distance from the objects is less for objects of the same material than for objects of different materials from each other. In the dendrogram (Figure 1), we can see that the classification into clusters is as follows:  
Figure 1: Cluster dendrogram of rock objects for sequence 500 and all attributes. One cluster on the second level, C1, contains concrete objects (c), two limestone objects (l), and one granite object (g). The first level cluster on the right C3 is compact and contains all andesite objects (a), two limestone objects (l), and two granite objects (g). The second-level cluster in the centre of dendrogram C2 contains limestone objects (l) and granite objects (g). The sorting efficiency by dendrogram is 48%. If we attempted to classify rocks using such vibration signal attributes for sequences with a length of 500 values, 19 objects out of 40 would be correctly classified. For the selected vibration signal attributes, we used the ANOVA test (one-factor ANOVA, where the factor is the type of material) and required related tests (Shapiro-Wilk test, Bartlett's test) to verify which attributes are applicable to distinguish between rocks. We also identified which attributes, if they are a part of objects, cause only noise and impair the possibility of distinguishing between materials (Table 3). To be suitable for distinguishing between materials, an attribute should have the following properties:  
* Rejected null hypothesis for ANOVA;
* Conditions fulfilled distribution normality for residues--Shapiro-Wilk test;
* Fulfilled conditions for homoscedasticity, the not rejected null hypothesis for [PERSON]'s test;
* All Tukey pairwise _p_adj values are less than 0.05. Only _Count Peaks_10_ meets all the criteria (Table 3). The box chart (Figure 2) shows that the attribute values overlap for individual materials--material cannot be classified using this attribute alone. Evaluation of findings: The vibration signal attributes used for the specified vibration signal length do not meet the classification purpose. In addition to the differences in the values of the attributes for a vibration signal time series of 1500 values, there are also differences in the variability of some of the attributes. For this reason, we created new objects from vibration signal attributes with a length of 1500, corresponding to a recording length of 1/12 of a second. The classification into clusters has been improved for the new sequence length of 1500 values, as shown in Figure 3. We can conclude that the Euclidean distance between individual concrete objects (c) in Cluster C1 is smaller than between objects of concrete and other materials. The Euclidean distance between objects of granite (g) and limestone (l) is as small as between individual objects of limestone (l) and individual objects of granite (g) (Cluster C3). To further improve the classification, we retained only attributes relevant to objects for sorting.