How K Means Clustering Algorithm Works Visually C#

Blog: Source Code: 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. - The Algorithm K-Means starts by randomly defining k centroids. From there, it works in iterative (repetitive) steps to perform two tasks: Assign each data point to the closest corresponding centroid, using the standard Euclidean distance. In layman’s terms: the straight-line distance between the data point and the centroid. For each centroid, calculate the mean of the values of all the points belonging to it. The mean value becomes the new value of the centroid. Once step 2 is complete, all of the centroids have new values tha