title: How to split set on equidistant subsets with maximum mean subset size?
body: <p>I have a set of N objects and N * N distances between them. I want to cluster this set on subsets, such that in each cluster there are all objects has the same distance and mean(cluster_size) on all clusters is maximized. </p>
  
  <p>I tried to solve this task by such algorithm:</p>
  
  <ol>
  <li><p>Lets enumerate all unique distances between objects.</p></li>
  <li><p>For each unique distance X lets build graph based with objects as nodes and adjacency matrix, where edge between A and B is present if distance between objects A and B is exactly X</p></li>
  <li><p>Lets find maximum clique in this graph. If size of this clique is bigger than current maximum - update maximum and store clique as Result</p></li>
  <li><p>Delete objects stored in Result from set of objects</p></li>
  <li><p>Repeat until set of objects is not empty</p></li>
  </ol>
  
  <p>Is there are any more efficient [approximate] solution? </p>