## 4 PER-SAC based Optimal Parameter Autotuning for DBSCAN
### PER-SAC Algorithm
The experimental results show that the proposed algorithm outperforms state-of-the-art DBSCAN algorithms in both clustering time and clustering accuracy, reducing the clustering time by up to 31.2% and improving the clustering accuracy by up to 8.8% on the tested AIS datasets. And it achieves more stable clustering results. Since the proposed PER-SAC algorithm uses NMI as the reward metric, it is currently only applicable to labeled datasets. Our future work will investigate the parameter autotuning method for unlabeled data to improve the generalizability of this work. Figure 11: Visualization results of different clustering algorithms with the 12K and 5-classified AIS dataset. **Author Contributions:** Conceptualization, X.A., Z.W. and S.L. ; methodology, X.A., Z.W. and D.W.; software, X.A., Z.W. and D.W.; validation, Z.W., D.W. and X.X. ; formal analysis, Z.W., X.X. and J.C.; investigation, Z.W., D.W. and J.C.; resources, X.A. and Z.W. ; data curation, Z.W., X.X. and J.C.; writing\(-\)original draft preparation, X.A. and Z.W. ; writing\(-\)review and editing, S.L. and C.J. ; visualization, Z.W., D.W. and J.C.; supervision, S.L. and C.J. ; project administration, S.L., C.J. and X.X. ; funding acquisition, S.L. and C.J. All authors have read and agreed to the published version of the manuscript. **Funding:** This research received no external funding. **Data Availability Statement:** Not applicable. **Conflicts of Interest:** The authors declare no conflict of interest. ## References

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