Outlier or anomaly detection is an important branch of data analysis that becomes a crucial task in many application domains. Data objects which significantly dissimilar and inconsistent from the rest of the data objects are referred to as an outlier. In this paper, a new approach, called LDBAD (Local Density-Based Abnormal Detector), is proposed to discover useful irregular patterns hidden in the collected data sets. This method aims to find local abnormal data objects, which are characterized through three proposed measurements: local distance, local density, and Influenced outlierness degree. The performance of the proposed approach is evaluated on flow pattern experiments along a 180 degrees sharp bend channel with and without a T-shaped spur dike. Flow velocity components are collected using 3D velocimeter Vectrino. The analysis shows that the novel outlier detection method is effective and applicable to find outlier objects. Moreover, some feed-forward neural network velocity prediction models are created to demonstrate the necessity and advantages of outlier detection in flow pattern experiments. The results show that the accuracy of created models has been increased by removing outliers from the measurements.

异常值或异常检测是数据分析的一个重要分支，在许多应用领域成为一项关键任务。与其余数据对象显着不同和不一致的数据对象被称为异常值。在本文中，提出了一种称为 LDBAD（基于局部密度的异常检测器）的新方法来发现隐藏在收集到的数据集中的有用的不规则模式。该方法旨在找到局部异常数据对象，通过三个建议的度量来表征这些对象：局部距离、局部密度和受影响的离群度。所提出方法的性能在沿 180 度急弯通道的流型实验中进行了评估，该通道带有和不带有 T 形丁坝。使用 3D 速度计 Vectrino 收集流速分量。分析表明，新的异常值检测方法是有效且适用于发现异常对象的。此外，还创建了一些前馈神经网络速度预测模型，以证明流型实验中异常值检测的必要性和优势。结果表明，通过从测量中去除异常值，创建的模型的准确性得到了提高。