## 4 Experiment, Results and Discussion
### Experimental Data
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Stage**} & **Number of** & \multirow{2}{*}{**Filter**} & \multicolumn{2}{c}{**F-Measure**} & \multirow{2}{*}{**Accuracy \%**} & \multirow{2}{*}{**Loss**} \\  & **Genes** & & & **Cluster 1** & & **Cluster 2** \\ \hline \multirow{4}{*}{I} & 6020 & \(50\times 121\) & \multirow{2}{*}{0.93} & \multirow{2}{*}{0.96} & \multirow{2}{*}{95} & \multirow{2}{*}{0.152} \\  & & \(25\times 242\) & & & & \\  & **3980** & \(50\times 80\) & \multirow{2}{*}{**0.96**} & \multirow{2}{*}{**0.98**} & \multirow{2}{*}{**0.97**} & \multirow{2}{*}{**0.146**} \\  & & \(25\times 160\) & & & & \\ \hline \multirow{4}{*}{II} & **3011** & \(50\times 61\) & \multirow{2}{*}{**0.96**} & \multirow{2}{*}{**0.98**} & \multirow{2}{*}{**0.97**} & \multirow{2}{*}{**0.197**} \\  & & \(25\times 122\) & & & & \\  & **3009** & \(50\times 61\) & \multirow{2}{*}{**0.96**} & \multirow{2}{*}{**0.98**} & \multirow{2}{*}{**0.165**} \\  & & \(25\times 122\) & & & & \\  & \(1934\) & \(50\times 39\) & \multirow{2}{*}{0.92} & \multirow{2}{*}{0.96} & \multirow{2}{*}{95} & \multirow{2}{*}{0.227} \\  & & \(25\times 78\) & & & & \\  & \(2046\) & \(50\times 41\) & \multirow{2}{*}{0.89} & \multirow{2}{*}{0.94} & \multirow{2}{*}{92} & \multirow{2}{*}{0.205} \\  & & \(25\times 82\) & & & & \\ \hline \multirow{4}{*}{III} & **1639** & \(40\times 41\) & \multirow{2}{*}{**0.96**} & \multirow{2}{*}{**0.98**} & \multirow{2}{*}{**0.97**} & \multirow{2}{*}{**0.156**} \\  & & \(20\times 82\) & & & & \\ \cline{1-1}  & \(1372\) & \(40\times 35\) & \multirow{2}{*}{0.95} & \multirow{2}{*}{0.96} & \multirow{2}{*}{95} & \multirow{2}{*}{0.199} \\ \cline{1-1}  & & \(20\times 70\) & & & & \\ \cline{1-1}  & **1519** & \(40\times 38\) & \multirow{2}{*}{**0.96**} & \multirow{2}{*}{**0.98**} & \multirow{2}{*}{**0.97**} & \multirow{2}{*}{**0.123**} \\ \cline{1-1}  & & \(20\times 76\) & & & & \\ \cline{1-1}  & & \(1490\times 50\) & \multirow{2}{*}{0.89} & \multirow{2}{*}{0.94} & \multirow{2}{*}{92} & \multirow{2}{*}{0.193} \\ \cline{1-1}  & & \(15\times 100\) & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation results regarding the step-by-step application of the hybrid inductive model of GEP clustering based on the SOTA clustering algorithm and CNN. Figure 8: Dendrogram of the GEP distribution into clusters with the classification accuracy of the objects containing as attributes the values of gene expressions of the appropriate clusters. stage, 10,000 GEPs were divided into two clusters, while for the second (smaller cluster), a high classification accuracy was achieved on the test data subset; 38 of 39 objects were correctly identified in this case. For the larger cluster, the classification accuracy was lower in terms of various criteria, with two objects out of 39 being falsely identified. However, it should be noted that the further division of the GEP contained in the smaller clusters into subsets consisting of 1934 and 2046 GEP worsened the classification accuracy, i.e., the division of the smaller cluster into subclusters is not reasonable in this case. Another conclusion can be reached in the case of dividing a larger cluster (6020 GEP) into subclusters. Dividing this cluster into two subsets (3011 and 3009 GEP) increases the classification accuracy of objects containing the values of gene expressions as attributes allocated in these clusters. Their further division into smaller subclusters allows us to allocate a subset of GEPs that are more informative according to the classification quality criteria for each branch. Practical Implementation of the Hybrid Inductive Model of GEP Clustering Based on the Spectral Clustering Algorithm  
Figure 9 shows the simulation results regarding the application of the proposed hybrid inductive clustering model based on the hierarchical spectral clustering algorithm. The number of clusters varied within the range from 2 to 10 during the simulation process's implementation.