title: Approaches for spatial geodesic latitude longitude clustering in R with geodesic or great circle distances
body: <p>I would like to apply some basic clustering techniques to some latitude and longitude coordinates.  Something along the lines of clustering (or some unsupervised learning) the coordinates into groups determined either by their <a href="http://en.wikipedia.org/wiki/Great-circle_distance">great circle</a> distance or their <a href="http://en.wikipedia.org/wiki/Geodesic_distance">geodesic</a> distance.  <em>NOTE: this could be a very poor approach, so please advise.</em></p>
  
  <p>Ideally, I would like to tackle this in <code>R</code>.</p>
  
  <p>I have done some searching, but perhaps I missed a solid approach?  I have come across the packages: <code>flexclust</code> and <code>pam</code> -- however, I have not come across a clear-cut example(s) with respect to the following:</p>
  
  <ol>
  <li>Defining my own distance function.</li>
  <li>Do either <code>flexclut</code> (via <code>kcca</code> or <code>cclust</code>) or <code>pam</code> take into account random restarts?</li>
  <li>Icing on the cake = does anyone know of approaches/packages that would allow one to specify the minimum number of elements in each cluster?</li>
  </ol>