## 1 Introduction
The above clustering algorithms are all dynamic clustering algorithms, which generally require a predetermined number of target sets to be divided and the determination of initial clustering centers for each type. Due to the significant impact of initial cluster centers on classification results, different initial cluster centers result in differences in the classification results. To this end, [PERSON] et al. [7] proposed an FCM clustering method for rock mass discontinuity sets using a genetic algorithm, which avoided the subjectivity of artificially delineating classification boundaries. [PERSON] et al. [19] adopted a combination of fuzzy equivalence clustering and fuzzy soft clustering, using the optimal grouping results obtained from the fuzzy equivalence clustering method as the initial division of the fuzzy soft clustering approach, and using the clustering center obtained from the soft clustering method as the central orientation of each classification. [PERSON] and [PERSON] [20] used a spectral clustering algorithm to construct an affinity matrix for similarity measurement between discontinuities, used the eigenvalue of the affinity matrix to construct space vectors corresponding to the projection poles of discontinuities, and then employed the K-means method for classification. [PERSON] et al. [PERSON] et al. (2018) adopted the K-means clustering algorithm to group the multi-property data of discontinuity sets and applied the principal component analysis approach to calculate the eigenvalue of the covariance matrix of the measured data. Additionally, statistical tools such as discriminant analysis, regressions, and decision analysis are used for exploratory analyses of structural plane data, and clustering methods that can combine other information besides structural plane orientation, such as planarity, weathering, spacing, or roughness, are proposed. Furthermore, methods of classifying structural plane sets using artificial intelligence technologies such as neural networks and machine learning have also been developed [PERSON] et al. (2018). However, without prior information on the groups, data must be classified into homogeneous object groups, and cluster analysis is the most suitable tool for applications [PERSON] et al. (2018). The clustering method is essentially a local search optimization approach, which is prone to getting stuck in local minima, thus dramatically affecting the clustering results of structural planes. Moreover, clustering analysis requires the number of clusters and fuzzy weighting index to be given in advance; how to scientifically and reasonably select these two parameters is currently an unresolved problem. Against this background, new technical means need to be introduced to solve the above problems. In the present study, an improved ABC algorithm is proposed and combined with the FCM clustering method, and a new FCM clustering method for automatically identifying dominant rock mass structural planes based on the ABC algorithm (named the FCM-ABC method) is developed. All the equations of this approach are fully developed, and the methodology is presented in its entirety. Moreover, the structural planes of rock masses are investigated at multiple depth levels in a gold mine in China by using a ShapeMetriX 3D system (3 GSM GmbH, Graz, Austria). Based on the measured data of structural planes, the specific calculation process, the selection of parameters, the effectiveness of grouping, and the determination of the dominant orientation of the proposed method for structural plane occurrence classification are analyzed and discussed, and multiple aspects including the higher recognition accuracy, faster convergence speed, and excellent performance of this method for the identification of structural plane sets compared to traditional clustering methods used in rock engineering applications are demonstrated. This method theoretically expands the technology of rock mass structural plane identification and has important application value in practical engineering.