Enhanced parallel implementation of the K-Means clustering algorithm

K-Means is one of the major clustering algorithms thanks to its simplicity and performance. Also, clustering is widely used in several applications that involve image processing, machine intelligence and others. This work discusses an enhanced parallel implementation of K-Means clustering using Cilk Plus and OpenMP on the CPU and CUDA on the GPU. The results are presented for different datasets and images of varying data sizes with concentration on relatively large data. Different numbers of features and clusters are also considered.