Cluster PCA for outliers detection in high-dimensional data

We introduce a new method to detect multiple outliers in high-dimensional datasets using the concepts of hierarchical clustering and principal component analysis. The proposed algorithm is computationally fast and robust to outliers detection. A comparative study with existing techniques is performed on both low and high dimensional datasets. Our experimental results demonstrate an improved performance of our algorithm in comparison with existing multivariate outlier detection techniques.