I'm trying to understand the DBSCAN implementation by scikit-learn, but I'm having trouble. Here is my data sample:
X = [[0,0],[0,1],[1,1],[1,2],[2,2],[5,0],[5,1],[5,2],[8,0],[10,0]]
Then I calculate D as in the example provided
D = distance.squareform(distance.pdist(X))
D returns a matrix with the distance between each point and all others. The diagonal is thus always 0.
Then I run DBSCAN as:
db = DBSCAN(eps=1.1, min_samples=2).fit(D)
eps = 1.1 means, if I understood the documentation well, that points with a distance of smaller or equal 1.1 will be considered in a cluster (core).
D[1] returns the following:
>>> D[1]
array([ 1. , 0. , 1. , 1.41421356,
2.23606798, 5.09901951, 5. , 5.09901951,
8.06225775, 10.04987562])
which means the second point has a distance of 1 to the first and the third. So I expect them to build a cluster, but ...
>>> db.core_sample_indices_
[]
which means no cores found, right? Here are the other 2 outputs.
>>> db.components_
array([], shape=(0, 10), dtype=float64)
>>> db.labels_
array([-1., -1., -1., -1., -1., -1., -1., -1., -1., -1.])
Why is there any cluster?
I figure the implementation might just assume your distance matrix is the data itself.
See: usually you wouldn't compute the full distance matrix for DBSCAN, but use a data index for faster neighbor search.
Judging from a 1 minute Google, consider adding metric="precomputed", since:
fit(X)
X: Array of distances between samples, or a feature array. The array is treated as a feature array unless the metric is given as ‘precomputed’.