From the previous post, I have shown how to calculate k-mean cluster using Tensorflow. In this post, I will add a bit more advanced implementations. In particular, I will show you how to implement conditional statement in Tensorflow.

The difference is at guessing the initial set of centroids. In the previous implementation, I simply chose k random points as initial centroids. Here, instead, I am selecting the first centroid to be the point furthest away from the origin. Next ith initial centroid for i = {1,2,...,k} is chosen such that the sum of the distances from previous i-1 centroids is the largest. This way, we can significantly reduce iteration number required to achieve the final state.

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