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I have n articles and a list of articles for each article implying similarity, e.g., a1 -> a3, a5 means a3 & a5 are similar to a1. Similarity here is 0 or 1 that is either two articles are similar or not; there is no floating point value.

Now, I want to cluster these articles into let's say k clusters. I can't directly apply KMeans to it, since these articles are not in vector form. Also I could try Spectral Clustering, but I don't understand in what form output is returned by Spectral Clustering (or Power Iteration Clustering).

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    $\begingroup$ Your data are presently in the format of adjacency list. But at any time it could be converted into the square matrix of pairwise similarity - if a program you are going to use requests it. But the question is another one. Your similarity values are just binary, yes and no. In other words, the graph is non-weighted. No clustering is really needed in your case (and K-means especially!) since clustering implies quantitative similarity (weighted graph). You should try some of many plotting techniques (including various networks) for non-weighted graphs. $\endgroup$
    – ttnphns
    Aug 15, 2015 at 11:39

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Also consider:

  • DBSCAN
  • Single-link (i.e. connected components)
  • Complete-link (i.e. Cliques)
  • Average-link (near cliques)

Plus, all the literature you can find on *graph clustering|.

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