## 1 Introduction  
Data mining denotes the process of extracting valuable knowledge and information from large datasets, with the aim of uncovering useful information hidden within the data. Clustering analysis is one of the important research directions in data mining and has applications in fields such as face recognition, image analysis, inventory forecasting, medical image processing, and text analysis [1, 2, 3, 4]. DBSCAN [5] is a classic density-based clustering algorithm that divides high-density regions into clusters and performs clustering in datasets with noise, allowing for arbitrary-shaped clusters. However, this algorithm has two drawbacks: first, it requires the manual specification of the parameters Eps and MinPts without prior knowledge; second, the choice of Eps and MinPts is crucial. Usually, the selection of MinPts parameters is greater than or equal to the data dimension plus one, and then the appropriate Eps is found through the \(k\)-distance graph, that is, the \"inflection point\" of the \(k\)-th nearest neighbor distance is identified as the candidate radius. Improper settings may lead to incorrect clustering results or even failure to cluster at all. To address the limitations of the DBSCAN algorithm, various scholars have proposed different solutions, among which parameter adaptation is a commonly used approach. [PERSON] et al. [6] improved DBSCAN based on the gray relational matrix (SAG-DBSCAN), which divides the dataset into dense and sparse subsets through local density measurement and clusters the dense parts using DBSCAN. [PERSON] et al. (2017) proposed an improved density clustering method by combining the particle swarm optimization (PSO) algorithm with DBSCAN. PSO automatically searches for the optimal combination of neighborhood radius (Eps) and minimum sample size (MinPts) parameters to enhance the robustness and clustering effect of the traditional DBSCAN algorithm to parameter sensitivity. [PERSON] et al. (2018) proposed an improved density clustering algorithm based on the nearest neighbor graph (NNG). The core idea is to directly identify local high-density samples by analyzing the inherent properties of the nearest neighbor graph and combine statistical methods to filter noise, ultimately adopting a clustering process similar to the DBSCAN algorithm. [PERSON] et al. (2019) proposed an optimization algorithm based on density clustering whose core innovation lies in the polynomial fitting and candidate parameter screening mechanism of \(k\)-dist graph, which is combined with a mathematical expectation method and contour coefficient verification to achieve dual parameter adaptive determination of Eps and MinPts, significantly improving the robustness of traditional DBSCAN to parameter sensitivity. [PERSON] et al. (2019) introduced an improved density clustering algorithm that combines graph neural networks (GNNs) and the DBSCAN algorithm. The core idea is to learn high-order neighborhood relationships between data points through GNNs, dynamically generate neighborhood density thresholds (such as replacing the traditional fixed parameter Eps), and optimize cluster boundary partitioning using graph structure information, thereby improving adaptability to complex topological data. [PERSON] et al. (2018) proposed an improved DBSCAN algorithm that combines the \(k\)-nearest neighbor (KNN) concept. Its core innovation lies in dynamically adjusting the local neighborhood density threshold using the \(k\)-nearest neighbor distance, replacing the fixed global parameter Eps of traditional DBSCAN, thereby enhancing its adaptability to data with uneven density. This algorithm typically generates candidate neighborhood radii automatically by constructing a KNN distance map and combines statistical methods to screen for the optimal parameter combination, achieving more robust clustering. [PERSON] et al. (2019) proposed RNN-DBSCAN, which is an improved density clustering algorithm based on reverse nearest neighbor (RNN) density estimation.