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I am hoping to use affinity propagation to cluster my data using sklearn. But I came across a question whether to use a distance matrix or similarity matrix in the fit method.

Please let me know what is suitable to use?

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  • $\begingroup$ Did you check the paper and the source code? $\endgroup$ – Has QUIT--Anony-Mousse Sep 19 '17 at 19:01
  • $\begingroup$ I did check the source code of sklearn. However, they are inserting some random data, so it is bit difficult to understand. $\endgroup$ – Volka Sep 20 '17 at 4:51
  • $\begingroup$ @Anony-Mousse Can you please tell me if you know? $\endgroup$ – Volka Sep 21 '17 at 0:58
  • $\begingroup$ I'd have to read the paper. $\endgroup$ – Has QUIT--Anony-Mousse Sep 21 '17 at 6:44
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According to sklearn.cluster.AP (in case in AP chosen affinity = "precomputed") the method "fit" requires X where X is matrix of similarities / affinities, that is, if distance matrix is estimated, use as:

X $ = matrix\ of\ similarities = -distance\ matrix$.

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