Understanding k-means clustering

The most essential clustering algorithm that OpenCV provides is k-means clustering, which searches for a predetermined number of k clusters (or groups) within an unlabeled multidimensional dataset.

It does so by using two simple assumptions about what an optimal clustering should look like:

• The center of each cluster is simply the arithmetic mean of all the points belonging to the cluster
• Each point in the cluster is closer to its own center than to other cluster centers

It's the easiest to understand the algorithm by looking at a concrete example.

Implementing our first k-means example

First, let's generate a 2D dataset containing four distinct blobs. To emphasize that this is...