On 12/06/2016 08:05 AM, Allan Visochek wrote:
At it's core, Markov clustering is a graph algorithm, it operates on a
sparse similarity matrix (essentially, by simulating flow between the
data points). This makes it useful for similarity graphs that don't
originate from features (i.e. protien-protien interaction networks).
Because the graph is based on similarity though, its definitely
possible to use it as a data clustering algorithm that takes a
similarity metric as an argument.
I suppose it could be implemented so that the algorithm could take
either a sparse similarity matrix or a set of features as its first
argument. This would keep the same structure of the other clustering
algorithms, but also allow use with pure similarity graphs. Does this
make sense?
Yeah that's also how the other algorithms work.
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