Numerical Example of K-Means Clustering

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