3
$\begingroup$

I have generated a dataset of pairwise distances as follows:

id_1 id_2 dist_12
id_2 id_3 dist_23

I want to cluster this data so as to identify the pattern. I have been looking at Spectral clustering and DBSCAN, but I haven't been able to come to a conclusion and have been ambiguous on how to make use of the existing implementations of these algorithms. I have been looking at Python and Java implementations so far.

Could anyone point me to a tutorial or demo on how to make use of these clustering algorithms to handle the situation in hand?

$\endgroup$
  • $\begingroup$ I just added an answer assuming that you want to cluster the samples id_1...id_n based on their distances. If you do want to cluster the distances themselves, you just need to use them as a 1-dimensional array. $\endgroup$ – logc Jul 8 '14 at 9:28
1
$\begingroup$

In the scikit-learn implementation of Spectral clustering and DBSCAN you do not need to precompute the distances, you should input the sample coordinates for all id_1 ... id_n. Here is a simplification of the documented example comparison of clustering algorithms:

import numpy as np
from sklearn import cluster
from sklearn.preprocessing import StandardScaler

## Prepare the data
X = np.random.rand(1500, 2)
# When reading from a file of the form: `id_n coord_x coord_y`
# you will need this call instead:
# X = np.loadtxt('coords.csv', usecols=(1, 2))
X = StandardScaler().fit_transform(X)

## Instantiate the algorithms
spectral = cluster.SpectralClustering(n_clusters=2,
                                      eigen_solver='arpack',
                                      affinity="nearest_neighbors")
dbscan = cluster.DBSCAN(eps=.2)

## Use the algorithms
spectral_labels = spectral.fit_predict(X)
dbscan_labels = dbscan.fit_predict(X)
$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.