K-means clustering

k-means clustering is a method of vector quantization, originally from signal processing, that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean (cluster centers or cluster centroid), serving as a prototype of the cluster. wikipedia

Back in 2016 during my master's degree, I had to explain this method to my colleagues in a presentation. So I made a quick visualization using javascript and HTML canvas API to help me go throw the algorithm step-by-step.

Try it:

1. Clone the project, in the terminal run:

$ git clone git@github.com:abachi/k-means.git

2. Open the index.html file with your browser.