## 3 Improved DBSCAN Algorithm Based on Sparrow Search Algorithm
### Algorithm Pseudocode and Flowchart
## 6 Conclusions

Clustering is a commonly used technique in data mining to discover natural distributions and potential structures in data. The DBSCAN algorithm is a widely used density-based clustering algorithm that can automatically identify noise and perform clustering in arbitrary shapes. However, the clustering quality is significantly influenced by the parameters. To address this issue, this paper proposes an SSA-optimized DBSCAN algorithm, SSA-DBSCAN, which employs the silhouette coefficient as the fitness function to optimize the DBSCAN input parameters Eps and MinPts. This approach addresses the algorithm's sensitivity to input parameters and allows DBSCAN to perform self-adaptive clustering. Performance tests on classic synthetic datasets, UCI real-world datasets, and image segmentation show that the clustering performance of SSA-DBSCAN outperforms that of the DBSCAN algorithm and its improved algorithms, such as KANN-DBSCAN, AF-DBSCAN, and other algorithms. Moreover, the proposed method has broad application prospects in fields such as remote sensing image analysis, medical image segmentation, video surveillance, environmental monitoring, and anomaly detection in high-dimensional data. However, the time complexity of SSA-DBSCAN remains similar to that of DBSCAN. Improving the running speed of algorithms requires additional research. **Author Contributions**: Methodology, S.Z. ([PERSON]) and S.Z. ([PERSON]); data curation, S.Z. ([PERSON]); resources, S.Z. ([PERSON]); software, Z.H. and Z.L. ; visualization, Z.L. ; writing--original draft, Z.H. ; and writing--review & editing, S.Z. ([PERSON]). All authors have read and agreed to the published version of the manuscript. **Funding**: This research received no external funding. **Data Availability Statement**: All research data used and provided in the paper are public. The needed sources are provided in the References. **Conflicts of Interest**: The authors declare no conflicts of interest. ## References

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