This article is considered to be the first to deal with the problem of outlier‐detection in multivariate circular data. The proposed algorithm is an extension of the Local Outlier Factor (LOF) method. Two different circular distances are used; taking into account the close bounded range of circular variables, and testing all possible permutations. The performance of the algorithm is investigated via an extensive simulation study. The performance of the LOF algorithm has a direct relationship with concentration parameter, while it has an inverse relationship with the sample size. For illustrative purposes, the algorithm has been implemented on two medical multivariate circular data, namely, X‐ray beam projectors data and eye data. The extension of the LOF algorithm for other types of directional data such as spherical and cylindrical datasets is worth to be investigated.

中文翻译：

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在医学多元循环数据中识别基于密度的局部离群值。

本文被认为是第一个处理多元循环数据中的异常值检测问题的文章。所提出的算法是局部离群因子（LOF）方法的扩展。使用了两种不同的圆形距离；考虑循环变量的封闭范围，并测试所有可能的排列。通过广泛的仿真研究来研究算法的性能。LOF算法的性能与浓度参数具有直接关系，而与样本量则呈反比关系。出于说明目的，该算法已在两个医学多元圆形数据上实现，即X射线束投影仪数据和眼睛数据。