This paper presents an optimized clustering method based on the mixed Fisher model to tackle several critical challenges

including inadequate characterization of orientation features

inconsistent evaluation standards

and poor integration with engineering applications. In this model

discontinuity orientations are treated as random variables that follow a Fisher distribution. The minimum message length (MML) criterion is employed for model selection and parameter estimation

balancing model complexity and data fit. Moreover

cuckoo search algorithm is utilized to optimize model parameters

enhancing global search capability

and preventing local optima. We compare the performance of the optimized clustering model with a control algorithm that does not utilize the MML criterion or the cuckoo search strategy

calculating the minimum objective function value over 50 iterations on synthetic datasets. The results demonstrate that the proposed clustering model exhibits superior accuracy and stability. To address the specific needs of high-level radioactive waste disposal

we combine the optimized clustering model with pole isodensity maps to determine the number of dominant sets for outcrop joints and borehole fractures. Furthermore

we generate synthetic data based on the clustering parameters of the model

providing a solid foundation for the subsequent reconstruction of the three-dimensional fracture network in the disposal rock mass.