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I am trying to identify 6 clusters in a graph of authors. Authors are connected with an edge when they have co-authored on a paper. I have already created a clustering with K-means (6-Means) and with Agglomerative clustering. Now, I want to combine these two clustering approaches in order to get more accurate clusters. I am able to find corresponding clusters, but I have no clue on how to decide to which cluster an author belongs when the algorithms do not agree.

Possibly I could use some form of a linear combination, but I do not know how to apply this properly. Other methods might be possible, but I am not sure on that.

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  • $\begingroup$ The bozo solution might be to run one algorithm then create an additional feature "cluster_label" and feed it into the next algorithm. You could then scale the "cluster_label" feature up or down until you get the result you want. Possibly experiment with the ordering of the two. $\endgroup$
    – AN6U5
    Jan 13 '16 at 19:59
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    $\begingroup$ en.wikipedia.org/wiki/Consensus_clustering $\endgroup$
    – Emre
    Jan 13 '16 at 20:14
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Since you have a graph of co-authors, it might make more sense to frame the problem at spectral clustering which is graph based.

Then, you can apply something like consensus clustering to combine the clusters.

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