# A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O\({}^{2}\) Facilities
## 4 Results and Discussion
### The Effect of Hyperparameter in the HDBSCAN
#### 4.3.2. \(Cluster\_Selection\_Epsilon\) \((\epsilon)\)  
Second, we examine \(cluster\_selection\_epsilon\), which determines the granularity of topic clustering. The various configurations are evaluated in Table 8. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Dataset** & **min\_cluster\_size** & **P** & **R** & **F1** & **FFR** & **FNR** \\ \hline Infoogger & 100 & **0.786** & 0.881 & **0.810** & **0.0009** & 0.1186 \\ (CERN) & 200 & 0.373 & 0.963 & 0.533 & 0.0043 & 0.0367 \\  & 300 & 0.333 & 0.992 & 0.498 & 0.0049 & 0.0085 \\  & 400 & 0.143 & 0.992 & 0.250 & 0.0147 & 0.0078 \\  & 500 & 0.142 & **0.993** & 0.249 & 0.0148 & **0.0067** \\ \hline BGL & 100 & 0.796 & 0.972 & 0.868 & 0.0200 & 0.0284 \\  & 200 & 0.909 & 0.974 & 0.939 & **0.0074** & 0.0259 \\  & 300 & 0.859 & 0.986 & 0.915 & 0.0126 & 0.0143 \\  & 400 & **0.910** & **0.987** & **0.944** & 0.0079 & **0.0129** \\  & 500 & 0.862 & 0.932 & 0.886 & 0.0122 & 0.0676 \\ \hline \hline \end{tabular} \(\overline{\text{P}}\) = Precision; R = Recall.  
\end{table} 
Table 7: Effect of min_cluster_size. Figure 7: Effect of min_cluster_size. The _min_samples_ parameter exhibited distinct performance patterns across both datasets. The InfoLogger dataset showed optimal performance at the lowest _min_samples_ values (100), achieving high precision while maintaining a high recall rate. However, a critical threshold was observed at _min_samples_ equal to 500, where the model experienced a severe performance degradation across all metrics. This pattern suggests that, while higher _min_samples_ values can enhance precision and reduce FPR, excessive values may lead to overgeneralization and missed cluster identification. Conversely, the BGL dataset displayed optimal performance at lower and higher _min_samples_ values (100 and 500), with peak metrics achieved at 500, followed by degradation performance in moderate _min_samples_ (200-400). The different behaviors of the two datasets, as visualized in Figure 9, indicate that _min_samples_ optimization is highly dataset-dependent, with InfoLogger favoring lower values for optimal performance while BGL benefits from higher _min_samples_ settings. The comprehensive analysis of the HDBSCAN's three primary parameters revealed a hierarchical influence on model performance. For the InfoLogger dataset, the _cluster_selection_epsilon_ demonstrated the most substantial impact, followed by _min_samples_, while _min_cluster_size_ exhibited the least significant effect for an optimal solution. For the BGL dataset, the _min_samples_ had the most effect, followed by _min_cluster_size_ and _cluster_selection_epsilon_, sequentially. This finding underscores the necessity of specific parameter tuning. In the case of InfoLogger, the primary challenge lies in the identification of small clusters, necessitating a precise adjustment of _cluster_selection_epsilon_, which directly aggregates these clusters into larger, more related groups. Conversely, for the BGL dataset, _min_samples_ appears to be more highly influenced by regulating cluster density. As a result, _cluster_selection_epsilon_ had a minimal impact \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Dataset** & **min\_samples** & **P** & **R** & **F1** & **FFR** & **FNR** \\ \hline InfoLogger & 100 & 0.810 & 0.946 & **0.871** & 0.0006 & 0.0539 \\ (CERN) & 200 & **0.903** & 0.751 & 0.793 & **0.0003** & 0.2487 \\  & 300 & 0.831 & 0.716 & 0.741 & 0.0004 & 0.2843 \\  & 400 & 0.686 & 0.562 & 0.617 & 0.0006 & 0.4380 \\  & 500 & 0.211 & **0.963** & 0.340 & 0.0102 & **0.0366** \\ \hline BGL & 100 & 0.920 & **0.972** & 0.945 & 0.0296 & **0.0279** \\  & 200 & 0.883 & 0.746 & 0.802 & 0.0069 & 0.2540 \\  & 300 & 0.918 & 0.695 & 0.782 & 0.0050 & 0.3048 \\  & 400 & 0.937 & 0.586 & 0.721 & 0.0137 & 0.4144 \\  & 500 & **0.958** & 0.947 & **0.952** & **0.0029** & 0.0534 \\ \hline \multicolumn{7}{l}{P = Precision; R = Recall.} \\ \end{tabular}
\end{table} 
Table 9: Effect of min_samples. Figure 9: Effect of min_samples. to no impact on the BGL dataset.