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    <p>A <em>lot</em> of work has been done in this area. The usual advice is to start with K-means clustering unless you have a really good reason to do otherwise - but K-means does <em>not</em> do hierarchical clustering (normally anyway), so you may have a good reason to do otherwise (although it's entirely possible to do hierarchical K-means by doing a first pass to create clusters, then do another pass, using the centroid of each of those clusters as a point, and continuing until you have as few high-level clusters as desired).</p> <p>There are quite a few other clustering models though, and quite a few papers covering relative strengths and weaknesses, such as the following:</p> <ol> <li><a href="http://books.nips.cc/papers/files/nips16/NIPS2003_AA24.pdf" rel="nofollow"><em>Pairwise Clustering and Graphical Models</em></a></li> <li><a href="http://vision.ucsd.edu/~sagarwal/bpc.pdf" rel="nofollow"><em>Beyond pairwise clustering</em></a></li> <li><a href="http://www.siam.org/proceedings/datamining/2009/dm09_068_yom-tove.pdf" rel="nofollow"><em>Parallel pairwise clustering</em></a></li> <li><a href="http://www.google.com/url?sa=t&amp;source=web&amp;cd=6&amp;sqi=2&amp;ved=0CEQQFjAF&amp;url=http://www.media.mit.edu/~brand/papers/brand-cluster.ps.gz&amp;rct=j&amp;q=pairwise%20clustering%20algorithms&amp;ei=y3gaTsrpPIi3sQKG4tDCBw&amp;usg=AFQjCNHy0UmcryLLsorQ1K7rFox3o3ipBQ" rel="nofollow"><em>A fast greedy pairwise distance clustering. algorithm and its use in discovering thematic. structures in large data sets.</em></a></li> <li><a href="http://w3.impa.br/~asla/quantiz/PCA.html" rel="nofollow"><em>Pairwise Clustering Algorithm</em></a></li> <li><a href="http://nlp.stanford.edu/IR-book/html/htmledition/hierarchical-agglomerative-clustering-1.html" rel="nofollow"><em>Hierarchical Agglomerative Clustering</em></a></li> </ol> <p>A little Googling will turn up lots more. Glancing back through my research directory from when I was working on clustering, I have dozens of papers, and my recollection is that there were a <em>lot</em> more that I looked at but didn't keep around, and many more still that I never got a chance to really even look at.</p>
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    1. POHierarchical clusterization heuristics
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    1. USJerry Coffin
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      1. VTUpMod
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    1. COThanks. Please look my question edit.
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    2. COThe fourth citation above is devoted primarily to the question you've added in your edit -- how to decide when to merge clusters vs. creating a "super-cluster" that encompasses both.
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