title: 2D k means clustering neural network
body: <p>I study atm Neural networks and one question keeps me awake.</p>
  
  <p>Let us assume we have the k means clustering problem in 2D. How would the actual network look like?</p>
  
  <p>I can find only that the updating rule would be something like w_i = w_i + epsilon(x - w_i). That is okay.</p>
  
  <p>Would it work if I do a simple net that looks like that:
  I have 2 inputneurons (2D) and each of them has a line to next level neurons, let us assume there are 3 Clusters so 3 neurons in the next layer. So would it work like that?
  2 neurons in inputlayer fully connected with 3 neurons in secondlayer... and my w_i would be the connection to the Cluster it belongs from both input neurons.</p>