k-means clustering is a method of vector quantization, originally from signal processing, that is popular for cluster analysis in data mining. k-means clustering aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean, serving as a prototype of the cluster. This results in a partitioning of the data space into Voronoi cells.
In this project, KMeans algorithm was implemented to find patterns in data. With varying number of centroids, classes of data is generated.
Sample plot using locations of Health Facilities in Ghana with k = 6.
