## 1 Introduction
By simulating whale predation behavior through the \"enclosure--bubble network attack--search\" strategy, the exploration and development capabilities of parameter search are balanced, thereby improving the clustering robustness of data with uneven density. [PERSON] et al. (2019) introduced AMD-DBSCAN, which employs the variance of neighborhood numbers to quantify the density variation across clusters. This approach requires only a single hyperparameter, minimizing redundant computations and demonstrating superior performance on datasets with varying density distributions. [PERSON] et al. (2020) proposed a novel clustering algorithm OBLOA-DBSCAN, which effectively solves the problem of the traditional DBSCAN algorithm being sensitive to parameters and finding it difficult to automatically determine core parameters, such as neighborhood radius Eps and minimum number of neighbors MinPts when processing complex data, by combining arithmetic optimization algorithm (AOA) with the inverse learning strategy (OBL). The algorithm first utilizes the OBL to improve the diversity of the initial population and the coverage of the search space. Then, through the powerful arithmetic-driven search mechanism of AOA, the parameter combination of DBSCAN is continuously optimized during the iteration process, resulting in significant improvements in accuracy, robustness, and adaptability in the final clustering results. These above methods for optimizing the DBSCAN algorithm have achieved relatively good results, but they still have some shortcomings and challenges. For example, the use of KNN or RNN may lead to different clustering results depending on the value of \(k\). KNN selects the distance at the inflection point as the Eps of DBSCAN by drawing the distance map of the \(k\)-th nearest neighbor, where usually \(k\) is set as the data dimension plus one. RNN is usually set based on time-series characteristics and selects appropriate values through experiments or visual analysis. Therefore, determining the appropriate \(k\) value is a topic that requires further research. In addition, setting MinPts parameters is also quite tricky, and currently most methods can only indirectly determine them through mathematical mapping or heuristic rules, lacking sufficient flexibility. Furthermore, \(k\)-nearest neighbor methods and metaheuristic algorithms have shortcomings in terms of computational complexity and convergence speed, which is particularly evident in scenarios involving large-scale data processing or real-time response. Some methods also have strong dependence on initial parameters, lack unified evaluation criteria, and poor adaptability to high-dimensional data. These factors collectively limit the algorithm's generalization ability and actual deployment effectiveness. Therefore, further improvements are still needed for DBSCAN and its optimization methods to achieve more efficient, robust, and adaptive clustering effects. Metaheuristic algorithms optimize complex problems by mimicking certain behaviors found in natural and social systems. The Sparrow Search Algorithm (SSA), proposed by [PERSON] [21], is a new metaheuristic algorithm that finds the optimal point by simulating the predation and anti-predation behaviors of sparrows. Due to the Sparrow Search Algorithm's few parameters and strong global search capabilities, this paper proposes an improved adaptive DBSCAN algorithm based on the Sparrow Search Algorithm (SSA-DBSCAN) which combines the rapid convergence and robust optimization performance of the Sparrow Search Algorithm (SSA) to dynamically adjust DBSCAN's parameters. The silhouette coefficient serves as the fitness function within the SSA framework, enabling the algorithm to identify the optimal cluster quantity and maintain high clustering quality. The result is a fully adaptive DBSCAN clustering process that does not require input parameters, offering advantages such as simplicity and high accuracy in clustering results.