K-means We first choose k initial centroids where k is a user-defined parameter, namely, the number of clusters desired Each point is then assigned to the closest centroid Each collection of points assigned to a centroid is a cluster. The centroid of each cluster is then updated based on the points assigned to the cluster. We repeat the assignment and update steps until the centroids remain the same 8/20/2016 PATTERN RECOGNITIONK-means We first choose K initial centroids, where K is a user-defined parameter, namely, the number of clusters desired. Each point is then assigned to the closest centroid. Each collection of points assigned to a centroid is a cluster. The centroid of each cluster is then updated based on the points assigned to the cluster. We repeat the assignment and update steps until the centroids remain the same. 8/20/2016 PATTERN RECOGNITION 14