DBSCAN and OPTICS are powerful algorithms for identifying clusters of points in domains where few assumptions can be made about the structure of the data. In this paper, we leverage these strengths and introduce a new algorithm, LINSCAN, designed to seek lineated clusters that are difficult to find and isolate with existing methods. In particular, by embedding points as normal distributions approximating their local neighborhoods and leveraging a distance function derived from the Kullback Leibler Divergence, LINSCAN can detect and distinguish lineated clusters that are spatially close but have orthogonal covariances. We demonstrate how LINSCAN can be applied to seismic data to identify active faults, including intersecting faults, and determine their orientation. Finally, we discuss the properties a generalization of DBSCAN and OPTICS must have in order to retain the stability benefits of these algorithms.

LINSCAN是一种新的算法，通过利用DBSCAN和OPTICS的优势，将点嵌入成近似其局部邻域的正态分布，并利用从Kullback Leibler散度派生的距离函数，以检测和区分在空间上密集但具有正交协方差的线性聚类，我们演示了如何将LINSCAN应用于地震数据，识别出活动断层，包括交叉断层，并确定它们的方向。最后，我们讨论了DBSCAN和OPTICS的扩展算法必须具备的性质，以保持这些算法的稳定性优势。

基于线性度量的聚类算法LINSCAN