title: R data clustering using a pre-defined distance/similarity matrix
body: <p>I developed a new document similarity measure (A method which calculate the amount of similarity/distance between two documents). I'm going to know how well this measure?</p>
  
  <p><strong>Clustering</strong> is an application which is based on a distance/similarity measure. So, I decided to evaluate the effectiveness of the proposed measure in different data clustering algorithms.</p>
  
  <p>I read about <a href="http://www.statmethods.net/advstats/cluster.html" rel="nofollow">different clustering algorithms in R</a>. Suppose I have a document collection <strong>D</strong> which contains <strong>n</strong> documents, organized in <strong>k</strong> clusters. I want to evaluate the application of my similarity/distance measure in a variety of clustering algorithms (partitional, hierarchical and topic-based). The problem is that all examples and tutorials start from a "<strong>data</strong>" matrix, but I have a <strong>distance/similarity</strong> matrix.</p>
  
  <p>Would you please help me with some hints in R?</p>