A RPCA-Based Tukey's Biweight for Clustering Identification on Extreme Rainfall Data

In high dimensional data, Principal Component Analysis (PCA)-based Pearson correlation remains broadly employed to reduce the data dimensions and to improve the effectiveness of the clustering partitions. Besides being prone to sensitivity on non-Gaussian distributed data, in a high dimensional data analysis, this algorithm may influence the partitions of cluster as well as generate exceptionally imbalanced clusters due to its assigned equal...