## 1 Introduction
By using \(k\)-nearest neighbor graph traversal instead of fixed radius neighborhood search in the traditional DBSCAN algorithm, the algorithm parameters are simplified to only select \(k\) values and the ability to handle density non-uniform datasets is enhanced. [PERSON] et al. (2019) proposed an improved DBSCAN algorithm based on dynamic adjustment of neighborhood density using \(k\)-nearest neighbors. The core idea is to adaptively generate local density thresholds by calculating the \(k\)-nearest neighbor distance distribution of each data point, replacing the fixed global parameter neighborhood radius (Eps) of the traditional DBSCAN algorithm, thereby improving the clustering robustness of data with uneven density. [PERSON] et al. (2019) proposed an innovative clustering method called partition KMNN-DBSCAN, aimed at solving the parameter setting problem of the traditional DBSCAN algorithm when dealing with data with uneven density or high noise. Firstly, the K-median nearest neighbor method was used to preliminarily partition the data, reducing the uncertainty of DBSCAN parameter selection and making the clustering effect more stable. However, even so, when dealing with large-scale or complex datasets, a single global clustering strategy may still face difficulties. Therefore, partition KMNN-DBSCAN further preprocesses the data by introducing a \"partition\" strategy, dividing the entire dataset into several subregions, with data in each subregion having a more uniform density distribution, and clustering separately based on the local features of each subregion. This partitioning method ensures that each local data subset can obtain more suitable \(\varepsilon\) and MinPts parameters, thereby improving the accuracy and efficiency of clustering. In each partition, partition KMNN-DBSCAN combines adaptive parameter selection by locally optimizing the parameters of each subset, ensuring that the most suitable clustering parameters can be automatically selected based on the density characteristics of local data. During the optimization process, the algorithm dynamically adjusts parameters by evaluating the clustering effect, making the final clustering results more in line with the actual distribution characteristics of the data. The partition clustering results will eventually be integrated into a complete global clustering structure, ensuring the completeness and accuracy of the final clustering. [PERSON] et al. (2015) introduced an improved adaptive density clustering algorithm (AF-DBSCAN), whose core innovation lies in fitting inflection points based on \(k\)-dist curve statistical distribution characteristics to adaptively select global parameters Eps and MinPts while reducing redundant calculations by optimizing regional query strategies, thereby improving the efficiency and parameter robustness of traditional DBSCAN. [PERSON] et al. (2016) introduced a density clustering algorithm for optimizing wafer manufacturing data. Its core innovation lies in combining spatial indexing to accelerate neighborhood search and improving the clustering accuracy of wafer surface temperature or defect distribution maps through adaptive parameter selection and data dimensionality reduction preprocessing, thereby optimizing anomaly detection and quality control in the wafer manufacturing process. [PERSON] et al. (2017) introduced an improved density clustering method that optimizes DBSCAN parameters or clustering process through genetic algorithm (GA), using factor analysis to group data. Its core objective is to solve the sensitivity of traditional DBSCAN to parameters through a global search strategy and improve adaptability to complex data distributions. [PERSON] et al. (2018) proposed an improved DBSCAN density clustering method based on the Whale Optimization Algorithm (WOA). Its core innovation lies in utilizing the global search capability of WOA to adaptively optimize the neighborhood radius Eps and minimum sample size MinPts of DBSCAN algorithm.