Hi all,
A colleague approached me today asking how the scikit-learn DBSCAN
algorithm should be applied and I must admit that the documentation
and example was confusing even to me. The fit docstring says
X: array [n_samples, n_samples] or [n_samples, n_features]
Array of distances between samples, or a feature array.
The array is treated as a feature array unless the metric is given as
'precomputed'.
However, the online demo does the following:
D = distance.squareform(distance.pdist(X))
S = 1 - (D / np.max(D))
db = DBSCAN().fit(S, eps=0.95, min_samples=10)
which uses a similarity matrix rather than a feature matrix as input
without passing metric="precomputed". Am I missing some interesting
clustering trick here, or is this a bug? I tried running the example
with the original feature matrix X (without tuning the parameters) and
it gave different output: all points were considered a single cluster
with no outliers.
TIA,
Lars
[1] http://scikit-learn.org/0.10/auto_examples/cluster/plot_dbscan.html
--
Lars Buitinck
Scientific programmer, ILPS
University of Amsterdam
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