Implemented in Python.
K-means Clustering Algorithm is a centroid-based technique which partitions a given unlabelled dataset into k clusters, creating high intra-class similarity and low inter-class similarity. Each cluster is represented by its center (i.e, centroid) which corresponds to the mean of points assigned to the cluster.
There are 3 main steps involved:
- Initialize centroids;
- Assign points to centroid;
- Update centroid location to cluster mean.