● 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
● Example:
● 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
● Example:
