Python implementation of k-means clustering algorithm with graphs created with matplotlib.
Run the program as "python kmeans.py". It opens and reads data from a file "input.txt" which is in the same directory. It shows this data on a 2D scatterplot to the user. Then it accepts number of clusters to create (k) and begins running the k-means algorithm. After it finishes execution, it shows a similar 2D plot but with points color coded based on the cluster they belong to. It will also show the corresponding cluster centres for each cluster - in its unique color.
As of now, the implementation is limited to 2 features in the input data i.e. 2 dimensional training sets.
The red points are each one training example represented by (X1i, X2i).
Each color i.e. Red, Blue, Green and Brown represents one cluster and the bold points represent their cluster centres.
- Python 2.7.x
- Library - matplotlib
- Library - numpy