Clustering (2) : Soft K-mean

Hard K-means and responsibilities If you did not read the first part of the clustering series. Please go check it out. I use the same data points and this post starts from troubleshooting the hard K-means algorithm in the previous post. In the previous post, we defined assignment. The equivalent representation of this assignment of points to clusters is given by responsibilities, $r^{(n)}_k$. In the assignment step, we set $r^{(n)}_k$ to one if mean k is the closest mean to datapoint ${\textbf x}^{(n)}$; otherwise, $r^{(n)}_k$ is zero.

Clustering (1), Hard K-means and its Failure

As I mentioned at the previous posting, one of the purposes of this blog is to supplement the Github of my data science study. I will gradually post and present all the iPython notebooks or Mathematica notebooks. I felt there’s no good Python tutorial for spectral clustering (at least from my search). Who can’t use scikit-learn among who is serious about machine learning. It was not difficult to find the theory of spectral clustering as well.

Namshik Kim

physicist, data scientist

Data Scientist