K-Means Clustering - Lazy Programmer

K-means clustering is one of the simplest clustering algorithms one can use to find natural groupings of an unlabeled data set. Another way of stating this is that k-means clustering is an unsupervised learning algorithm. "learning the structure of X without being given Y". K-means clustering finds "k" different means (surprise surprise) which represent the centers of k clusters and assigns each data point to one of these clusters. The cluster it is assigned to is the one where the distance (usually Euclidean) from the point to the mean is smallest.