title: clustering with cosine similarity
body: <p>I have a large data set that I would like to cluster. My trial run set size is 2,500 objects; when I run it on the 'real deal' I will need to handle at least 20k objects. </p>
  
  <p>These objects have a cosine similarity between them. This cosine similarity does not satisfy the requirements of being a mathematical distance metric; it doesn't satisfy the triangle inequality. </p>
  
  <p>I would like to cluster them in some "natural" way that puts similar objects together without needing to specify beforehand the number of clusters I expect.</p>
  
  <p>Does anyone know of an algorithm that would do that? Really, I'm just looking for any algorithm that doesn't require a) a distance metric and b) a pre-specified number of clusters.</p>
  
  <p>Many thanks!</p>
  
  <p>This question has been asked before here:
  <a href="https://stackoverflow.com/questions/5841282/clustering-from-the-cosine-similarity-values">Clustering from the cosine similarity values</a>
  (but this solution only offers K-means clustering), and here:
  <a href="https://stackoverflow.com/questions/10086551/effective-clustering-of-a-similarity-matrix">Effective clustering of a similarity matrix</a>
  (but this solution was rather vague)</p>