Outlier detection is one of the most important research directions in data mining. However, most of the current research focuses on outlier detection for categorical or numerical attribute data. There are few studies on the outlier detection of mixed attribute data. In this article, we introduce fuzzy rough sets (FRSs) to deal with the problem of outlier detection in mixed attribute data. Since the outlier detection model of the classical rough set is only applicable to the categorical attribute data, we use FRS to generalize the outlier detection model and construct a generalized outlier detection model based on fuzzy rough granules. First, the granule outlier degree (GOD) is defined to characterize the outlier degree of fuzzy rough granules by employing the fuzzy approximation accuracy. Then, the outlier factor based on fuzzy rough granules is constructed by integrating the GOD and the corresponding weights to characterize the outlier degree of objects. Furthermore, the corresponding fuzzy rough granules-based outlier detection (FRGOD) algorithm is designed. The effectiveness of the FRGOD algorithm is evaluated through experiments on 16 real-world datasets. The experimental results show that the algorithm is more flexible for detecting outliers and is suitable for numerical, categorical, and mixed attribute data.

中文翻译：

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混合属性数据中基于模糊粗糙粒子的离群值检测。

离群检测是数据挖掘中最重要的研究方向之一。但是，当前的大多数研究都集中在分类或数值属性数据的异常检测上。关于混合属性数据的异常检测的研究很少。在本文中，我们介绍了模糊粗糙集（FRS）来解决混合属性数据中离群值检测的问题。由于经典粗糙集的离群值检测模型仅适用于分类属性数据，因此我们使用FRS对该离群值检测模型进行泛化，并基于模糊粗糙粒子构造一个广义的离群值检测模型。首先，通过使用模糊逼近精度定义颗粒离群度（GOD）来表征模糊粗糙颗粒的离群度。然后，通过将GOD和相应的权重相结合，构造了基于模糊粗糙颗粒的离群因子，以表征物体的离群度。此外，设计了相应的基于模糊粗糙粒子的离群值检测（FRGOD）算法。通过对16个真实数据集的实验评估了FRGOD算法的有效性。实验结果表明，该算法对异常值的检测更加灵活，适用于数值，分类和混合属性数据。