Proposing a new local density estimation outlier detection algorithm: an empirical case study on flow pattern experiments

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.