K-means

K-means might be the most well-known clustering algorithm. Using the number of clusters k as parameter, k-means aims to iteratively minimize the within-cluster-sum-of-squares by assigning each object to the closest centroid (an artificial object) followed by a subsequent update of these centroids. The assignment after the convergence of this process is reported as clusters.