I found a very nice and interactive visualization of K-means clustering algorithm, so thought of sharing it!
The algorithm is composed of the following steps:
- Place K points into the space represented by the objects that are being clustered. These points represent initial group centroids.
- Assign each object to the group that has the closest centroid.
- When all objects have been assigned, recalculate the positions of the K centroids.
- Repeat Steps 2 and 3 until the centroids no longer move. This produces a separation of the objects into groups from which the metric to be minimized can be calculated.
Hope you would like it!