CODEWITHSUNDEEP
pythonmachine-learningscikit-learncluster-analysisdbscan
user8510273
user8510273

Reputation:

Get the cluster size in sklearn in python

I am using sklearn DBSCAN to cluster my data as follows.

#Apply DBSCAN (sims == my data as list of lists)
db1 = DBSCAN(min_samples=1, metric='precomputed').fit(sims)

db1_labels = db1.labels_
db1n_clusters_ = len(set(db1_labels)) - (1 if -1 in db1_labels else 0)
#Returns the number of clusters (E.g., 10 clusters)
print('Estimated number of clusters: %d' % db1n_clusters_)

Now I want to get the top 3 clusters sorted from the size (number of data points in each cluster). Please let me know how to obtain the cluster size in sklearn?

Upvotes: 7

Views: 10415

Answers (2)

σηγ
σηγ

Reputation: 1314

Another option would be to use numpy.unique:

db1_labels = db1.labels_
labels, counts = np.unique(db1_labels[db1_labels>=0], return_counts=True)
print labels[np.argsort(-counts)[:3]]

Upvotes: 4

Gambit1614
Gambit1614

Reputation: 8801

Well you can Bincount Function in Numpy to get the frequencies of labels. For example, we will use the example for DBSCAN using scikit-learn: 

#Store the labels
labels = db.labels_

#Then get the frequency count of the non-negative labels
counts = np.bincount(labels[labels>=0])

print counts
#Output : [243 244 245]

Then to get the top 3 values use argsort in numpy. In our example since there are only 3 clusters, I will extract the top 2 values :

top_labels = np.argsort(-counts)[:2]

print top_labels
#Output : [2 1]

#To get their respective frequencies
print counts[top_labels]

Upvotes: 4

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