Hi Johannes.
I think the example is just wrong. Can someone confirm this?

Cheers,
Andy

On 04/04/2013 06:57 PM, Johannes Knopp wrote:
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> Hi everyone,
>
> I just stumbled upon the example plot_dbscan.py at [1]. As far as I
> understand, the similarity matrix S is computed from the data in X and
> then it is used for clustering with DBSCAN. What confused me was that
> the documentation for DBSCAN.fit(X) says that it takes a *distance*
> matrix.
>
> Here is the code snippet:
>
> - ------------------------
> # Compute similarities
> D = distance.squareform(distance.pdist(X))
> S = 1 - (D / np.max(D))
>
> # Compute DBSCAN
> db = DBSCAN(eps=0.95, min_samples=10).fit(S)
> - ------------------------
>
> Shouldn't it be "[?].fit(D)" instead?
>
> I would be happy if anybody could explain if my understanding is wrong
> or if the example is flawed.
>
> Best regards,
>
> Johannes
>
> [1]
> http://scikit-learn.org/dev/auto_examples/cluster/plot_dbscan.html#example-cluster-plot-dbscan-py
> [2]
> http://scikit-learn.org/dev/modules/generated/sklearn.cluster.DBSCAN.html#sklearn.cluster.DBSCAN
>
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