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Trying to use affinity propagation for a simple clustering task:

from sklearn.cluster import AffinityPropagation
c = [[0], [0], [0], [0], [0], [0], [0], [0]]
af = AffinityPropagation (affinity = 'euclidean').fit (c)
print (af.labels_)

I get this strange result: [0 1 0 1 2 1 1 0]

I would expect to have all samples in the same cluster, like in this case:

c = [[0], [0], [0]]
af = AffinityPropagation (affinity = 'euclidean').fit (c)
print (af.labels_)

which indeed puts all samples in the same cluster: [0 0 0]

What am I missing?

Thanks

Baba
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1 Answers1

4

I believe this is because your problem is essentially ill-posed (you pass lots of the same point to an algorithm which is trying to find similarity between different points). AffinityPropagation is doing matrix math under the hood, and your similarity matrix (which is all zeros) is nastily degenerate. In order to not error out, the implementation adds a small random matrix to the similarity matrix, preventing the algorithm from quitting when it encounters two of the same point.

Andreus
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