A comparison among data mining algorithms for outlier detection using ow pattern experiments

Accurate outlier detection is an important matter to consider prior to applying data to predict ow patterns. Identifying these outliers and reducing their impact on measurements could be eective in presenting an authentic ow pattern. This paper aims to detect outliers in ow pattern experiments along a 180-degree sharp bend channel with and without a T-shaped spur dike. Velocity components have been collected using 3D velocimeter called Vectrino in order to determine the ow pattern. Some of outlier detection methods were employed in the paper, such as Z-score test, sum of sine curve tting, Mahalanobis distance, hierarchical clustering, LSC-mine, self-organizing map, fuzzy C-means clustering, and voting. Considering the experiments carried out, the methods were ecient in outlier detection; however, the voting method appeared to be the most ecient one. Brie y, this paper calculated dierent hydraulic parameters in the sharp bend and made a comparison between them for the sake of studying how eective running the voting method is in mean and turbulent ow pattern variations. The results indicated that developing the voting method in the ow pattern experiment in the bend would cause a decrease in Reynolds shear stress by 36%, while the mean velocities were not signicantly in uenced by the method