ASSG 4 - Kmeans Algorithm

Kmeans Algorithm Assignment

Part 1

Load samples from “jain_feats.txt” into a 2d numpy array X. [For N samples shape should be Nx2]
Load initial centroids from “jain_centers.txt” into another 2d numpy array centroid_old. [For two centroids shape should be 2x2]
Take another 2d numpy array named centroid_new and initialize it with zeros. [For two centroids shape should be 2x2]
The initial scatter plot containing X and centroid_old should look like this:

Part 2

Take a 1D numpy array named label with size equals to number of rows in X

Assign points to centroids/clusters:

    For each row i in X:
        Take a 1D numpy array named dist with size equals to number of rows in centroid_old 
        For each row j in centroid_old:
          Assign dist[ j ] := distance between X[ i, :] and centroid_old[ j, :]
        label[ i ] := j, for which dist[ j ] is minimum [Can easily done by numpy argmin method]

Update Centroids:

    For each row j in centroid_new:
        Assign centroid_new[ j ] := Average(X[ label == j]) [Can easily done by numpy methods]

Stop condition check:

    If:
        For each row j in centroid_new:
	Calculate difference between centroid_new[ j ] and centroid_old[ j ]
        If the maximum value among differences found above is less than 1E-7: STOP
    Else: 
        centroid_old := centroid_new
        MOVE to next Iteration

Part 3

Finally centroid_old array holds the final cluster centroids and
	label array holds the final assignments to clusters

The final plot should look similar to the following:

Solution : LINK