关键词: 岩体结构面, 优势产状, 聚类集成, 具有噪声的基于密度的聚类（DBSCAN）, 轮廓系数

Abstract: For the problems existing in the traditional single discontinuity based clustering model, such as the risk of misclassification or omission and the inability to identify noise and isolated values, a dominant partitioning method of rock mass discontinuity based on selective clustering ensemble using density-based spatial clustering of applications with noise (DBSCAN) algorithm is proposed. Firstly, the spatial coordinate transformation is performed with the attitude of discontinuity, and the sine of the angle between the unit normal vectors is defined as similarity measurement. Then, a certain number of different base clusters are constructed based on the DBSCAN algorithm, with the selective clustering ensemble technology, some excellent base clusters are selected. Finally, the consistent ensemble technology is used to fuse these base clusters to generate a highly reliable selective clustering ensemble result. The DIPS software data set and the discontinuity survey result in the dam site area of Songta hydropower station are used to test the feasibility and effectiveness of the proposed method. The research results show that the clustering effect of the proposed method is significantly better than that of common clustering algorithms. The clustering results are objective and reasonable. It not only effectively identifies noise and isolated values, but also overcomes the shortcomings of over-segmentation or under-segmentation of the single discontinuity based clustering model. The research results are valuable in accurately determining the dominant group of discontinuity.

Key words: rock mass discontinuity, dominant attitude, clustering ensemble, density-based spatial clustering of applications with noise (DBSCAN), silhouette coefficient