The basic step of k-means clustering is simple. In the beginning we determine number of cluster K and we assume the centroid or center of these clusters. We can take any random objects as the initial centroids or the first K objects in sequence can also serve as the initial centroids.
Then the K means algorithm will do the three steps below until convergence
Iterate until stable (= no object move group):
- 1、Determine the centroid coordinate
- 2、Determine the distance of each object to the centroids
- 3、Group the object based on minimum distance
