## 6 Methodology
### Cauterization Analysis and Discussion
By analyzing the distribution of cluster cardinalities across multiple runs, we can gain insights into the sensitivity of the clustering process and potentially improve the reliability of the results. It can be noticed in Figure 26 that the dependence of the number of clusters on the maximum radius parameter for the OPTICS algorithm applied to the OP3 data was downsampled by 20. OPTICS is a clustering algorithm that can identify clusters of varying densities in data. Unlike DBSCAN (which we discussed earlier), OPTICS does not require pre-definition of the number of clusters or a minimum number of points in a cluster (MinPts). Figure 26: Dependence of the number of clusters on the maximum radius parameter: OPTICS algorithm for OP3 downsampled by 20. Figure 25: Properties of the hourly status vectors in OP3, downsampled by 20: cardinality of the DBSCAN clusters for 100 runs. It focuses on identifying points that are density-reachable from each other, and then clusters are formed based on these reachability relationships. The graph shows a curve that starts high on the left and decreases as the maximum radius increases on the x-axis. With a larger maximum radius (Eps), more data points are considered reachable from a given point, potentially leading to the merging of smaller, denser clusters into larger ones, resulting in fewer overall clusters. Conversely, a smaller maximum radius creates stricter reachability criteria, causing data points to be separated into more numerous, smaller clusters. By applying OPTICS and analyzing how the number of clusters varies with the maximum radius, you can gain insights into the inherent groupings or patterns within the status vectors. Clusters might represent different operational modes of OP3. The radius value that yields a stable number of clusters (e.g., neither too high nor too low) could indicate the range corresponding to normal operating conditions. The way clusters merge or separate with varying radius values can provide clues about how the different status vector elements interact and influence each other. OPTICS can handle data with varying densities more effectively than DBSCAN, which might struggle with clusters of different shapes and sizes. It does not require pre-definition of the number of clusters or MinPts, making it more flexible for exploratory data analysis. By analyzing how the OPTICS cluster structure changes with the maximum radius, you can gain insights into the system's behavior and potentially identify normal operating ranges, anomalies, or underlying relationships within the data. The Juno orbit events were clusterized with unsupervised machine learning DBSCAN and OPTICS algorithms. This mechanism separated outliers from the core part to conform to the accuracy values included in Table 5.  
The five-dimensional status vector is \(U(t)\). The minimum value of the EE identification accuracy was detected in the OP3 as being 97.4% and 98.9% for the DBSCAN and OPTICS, respectively. The reason for the lowest accuracy in the OP3 is the high number of perjoyes executed by Juno around Jupiter. We measured that the OPTICS clusterization mechanism needed a 10.8 times longer execution time than the DBSCAN, showing the significantly higher expense of the unsupervised calculations.