CODEWITHSUNDEEP
pythonscikit-learncluster-analysisk-means
data_person
data_person

Reputation: 4470

K means clustering in scikit learn

I am trying to do k means clustering in scikit learn. Code:

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters = 10)
x = df.values
kmeans.fit(x.reshape(-1, 1))

If the parameter n_init = random, it chooses random initial centroids. Is there a way to fetch the initial centroids used?

Upvotes: 3

Views: 4120

Answers (2)

MMF
MMF

Reputation: 5929

You can only get your cluster centers after fitting the KMeans object to your data.

Little trick !

So what you can do is set the parameter max_iter to 1. By default it is set to 300 and then centers may change at each iteration.

If you use only one iteration the algorithm will assign each sample to an initial center and then it will stop, not updating the centers.

Thus calling .cluster_centers_ will give back the initial centroids !

Upvotes: 3

xtt
xtt

Reputation: 917

Yes, I assume you can try centroids = kmeans.cluster_centers_ before calling fit()

Upvotes: -2

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