Visualizing K-Means algorithm

K-Means is a popular and simple clustering algorithm. K-means takes an unsupervised approach, which means that the input data points are not labelled, and the algorithm figures out the patterns on its own.

Steps to execute kmeans

Initialization: K centroids are randomly initialized
Assignment step: Each of the N data-points is assigned to the nearest cluster (centroid)
Update step: When all N nodes have been assigned, the new K centroids are computed by taking the average of the datapoints in each cluster
Go to step 2 and repeat until the datapoints stop changing clusters

Given below is a simulation of K-Means. Scroll below to find the buttons and instructions for interacting with this tool

N (the number of node):

K (the number of cluster):

Click figure or push [Step] button to go to next step.
Push [Restart] button to go back to initialization step.
Push [New] button to start new simulation with given N (the number of nodes) and K (the number of clusters).

Created using D3.js and ActionScript 3