# Dominant Partitioning of Discontinuities of Rock Masses Based on DBSCAN Algorithm
## 1 Introduction
[PERSON] [21] integrated the fuzzy K-means algorithm and spectral clustering algorithm and proposed a new dominant grouping method for rock mass discontinuities. [PERSON] et al. [22] tried to find the optimal grouping method by analyzing the advantages and disadvantages of three methods (traditional graphic method, fuzzy equivalent clustering and FCM clustering) in discontinuity orientation grouping. [PERSON] et al. [23] improved the clustering center algorithm, then a fuzzy C-means clustering algorithm based on rough set was proposed and applied to the study of advantage grouping. [PERSON] [24] and [PERSON] [25] used genetic, ant colony and other heuristic algorithms to continuously optimize and search for global optimal solutions to solve the problem of FCM clustering results being sensitive to initial clustering centers and easily falling into local optimal solutions. However, this method requires encoding and decoding, and the operation is more complicated. In order to overcome this problem, [PERSON] et al. [26] proposed a hierarchical clustering analysis method for discontinuity occurrence, which did not need to determine the clustering center in advance and had a fast convergence speed and good clustering effect. Analyzing the above, it can be noted that the K-means algorithm and fuzzy K-means algorithm are dynamic clustering algorithms. Compared with other methods, these two methods are not easily affected by outliers, so they are more suitable for the dominant grouping of rock mass discontinuities. However, these two methods require the initial clustering center in advance when grouping, and the grouping results are easily affected by the initial clustering center. In addition, these two methods are local optimal algorithms, and the final grouping result is often local optimal, rather than global optimal. At the same time, these algorithms still need to determine the number of rock mass discontinuities in advance, and it is difficult to identify the noise points and solitary values in the occurrence of discontinuities [27; 28]. A key application for the dominant grouping of discontinuities is to construct a three-dimensional fracture network model; specifically, according to the grouping results, use statistical methods to obtain the probability density function of discontinuity indexes, and use the Monte-Carlo random simulation method to build a three-dimensional random fracture network model that obeys statistical laws on the computer ([PERSON] et al., 2021, [PERSON] et al., 2023, [PERSON] et al., 2022) [29; 30; 31]. From the perspective of sample density, the density-based clustering with noise (DBSCAN) [32] algorithm obtains clustering results based on the connectivity between samples, and has the function of filtering out noise and isolated values. In recent years, it has been applied to the field of rock point cloud data structure surface classification [33], but there is little targeted research in the dominant occurrence grouping of rock mass discontinuities. The basic idea is that for each object in a cluster, the number of objects contained in the neighborhood of its given radius cannot be less than a given minimum number, and the objects with density connection characteristics are clustered. The advantage is that it can mine clusters of any shape, is insensitive to the order of data input and has the ability to handle abnormal data. The density-based clustering algorithm has the characteristics of discovering clusters of arbitrary shapes and being insensitive to noise data and data input order, which can make up for the K-mean algorithm in the initial center point, and it is difficult to determine shortcomings. It can effectively eliminate the noise and isolated value in rock mass discontinuity occurrence data and avoid single problems such as misjudgment or missed selection of a model [31].