Parallel Visual Assessment of Cluster Tendency on GPU

Visual Approaches for Exploratory Data Analysis: A Survey of the Visual Assessment of Clustering Tendency (VAT) Family of Algorithms

“…The massively parallel computing capability of CUDAenabled GPUs was exploited by Meng and Yuan [32] to develop a GPU-accelerated VAT, which improved the computational efficiency of VAT using a parallel implementation. Along similar lines, edge-based VAT (eVAT), an edge-based algorithm that can replicate the output of iVAT [14] but is more efficient and more suitable for parallelism, was proposed by Meng and Yuan in [33].…”