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

IterateStable(= 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