Getting Started with Orange 12: k-Means Explained Video Lecture | Orange Data for Beginners - Data & Analytics

In the previous video we talked about k-Means clustering
and how we can find interesting groups in our data.
But how does k-Means actually work?
That's easy to find out.
We can use interactive k-Means,
from our Educational add-on.
This add-on is designed for teaching machine learning
and it includes some wonderful instructive widgets.
To install it go to Options,
choose Add-ons,
and select Educational.
You will need to restart Orange
to make the widgets from this add-on active.
We will first plot a data set
with three groups of data instances
to see if we can retrieve them with k-Means.
Now connect Paint Data to Interactive k-Means.
Besides the data,
the widget also plots the centroids marked with squares,
each one in its own color.
Centroids are the assumed centers of clusters.
Interactive k-Means places them randomly.
Notice that each data point is associated with the closest centroid.
In this way, the centroids define the clusters.
But while we have three clusters,
they are not the ones we would wish for.
First, it would be better to move the centroids to the center of data points.
We do this by pressing Recompute Centroids button.
The centroids moved.
Look at the red centroid.
Some green andblue points are closer to it into the other two centroids.
We need to reassign centroid membership
so that the data instances are labeled with the closest centroid.
We do this by pressing Reassign Membership.
We can again move the centroid to the center of its data instances
using Recompute Centroids
and again reassign the membership.
We repeat these two steps until convergence,
that is until the position of the centroids does not change.
For our data the convergence took only a few steps
and the algorithm found the appropriate clusters.
Now let us try to place centroids, so the algorithm would fail.
Here, I will put the red centroid in between two groups of data points
The green and the blue centroid
so that they share the remaining group.
Now press Recompute Centroids
and Reassign Membership a couple of times.
The algorithm converged,
but the clustering it found
is not the one we had wished for.
Today we've learned how k-Means finds clusters
with the help of an interactive visualization.
We also learned
that the initial placement of centroids is important,
This is why k-Means normally uses some heuristic for smart placement
and re-runs the algorithm a couple of times
to report only on the best clustering.