CODEWITHSUNDEEP
pythonmachine-learningscikit-learncluster-analysisk-means
vij555
vij555

Reputation: 359

How to identify Cluster labels in kmeans scikit learn

I am learning python scikit. The example given here displays the top occurring words in each Cluster and not Cluster name.

http://scikit-learn.org/stable/auto_examples/document_clustering.html

I found that the km object has "km.label" which lists the centroid id, which is the number.

I have two question

1. How do I generate the cluster labels?
2. How to identify the members of the clusters for further processing.

I have working knowledge of k-means and aware of tf-ids concepts.

Upvotes: 18

Views: 63925

Answers (2)

ArmandduPlessis
ArmandduPlessis

Reputation: 939

Oh that's easy

My environment: scikit-learn version '0.20.0'

Just use the attribute .labels_ as in the docs: https://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html

from sklearn.cluster import KMeans
import numpy as np

Working example:

x1 = [[1],[1],[2],[2],[2],[3],[3],[7],[7],[7]]
x2 = [[1],[1],[2],[2],[2],[3],[3],[7],[7],[7]]

X_2D = np.concatenate((x1,x2),axis=1)

kmeans = KMeans(n_clusters=4, init='k-means++', max_iter=300, n_init=10, random_state=0)
labels = kmeans.fit(X_2D)

print(labels.labels_)

Output:

[2 2 3 3 3 0 0 1 1 1]

So as you can see, we have 4 clusters, and each data example in the X_2D array is assigned a label accordingly.

Upvotes: 6

IVlad
IVlad

Reputation: 43477

  1. How do I generate the cluster labels?

I'm not sure what you mean by this. You have no cluster labels other than cluster 1, cluster 2, ..., cluster n. That is why it's called unsupervised learning, because there are no labels.

Do you mean you actually have labels and you want to see if the clustering algorithm happened to cluster the data according to your labels?

In that case, the documentation you linked to provides an example:

print("Homogeneity: %0.3f" % metrics.homogeneity_score(labels, km.labels_))
print("Completeness: %0.3f" % metrics.completeness_score(labels, km.labels_))
print("V-measure: %0.3f" % metrics.v_measure_score(labels, km.labels_))
  1. How to identify the members of the clusters for further processing.

See the documentation for KMeans. In particular, the predict method:

predict(X)

Parameters: X : {array-like, sparse matrix}, shape = [n_samples, n_features] New data to predict.

Returns:
labels : array, shape [n_samples,] Index of the cluster each sample belongs to.

If you don't want to predict something new, km.labels_ should do that for the training data.

Upvotes: 10

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