Overview on algorithm

Basic K-Means Algorithm #

This is how the standard algorithm work

1) Initialization: select K points as the initial centroids
i) repeat  i
    i.1) Assignment step: form K clusters by assigning all points to closest centroid
    i.2) Update step: recompute the centroid of each cluster
until) stopping conditions are met

Details

• K is the number of clusters
• centroid is the mean of the points in cluster
• closeness is measured by a distance based on some features of points
• stopping conditions deal with convergence (centroids don't change, few points change clusters,...)