kmeans sklearn
from sklearn.cluster import KMeans
import numpy as np
X = np.array([[1, 2], [1, 4], [1, 0],
[10, 2], [10, 4], [10, 0]])
kmeans = KMeans(n_clusters=2, random_state=0).fit(X)
kmeans.labels_
kmeans.predict([[0, 0], [12, 3]])
kmeans.cluster_centers_k-means clustering python
from sklearn.cluster import KMeans
kmeans = KMeans(init="random", n_clusters=3, n_init=10, max_iter=300, random_state=42 )
kmeans.fit(x_train) #Replace your training dataset instead of x_train
# The lowest SSE value
print(kmeans.inertia_)
# Final locations of the centroid
print(kmeans.cluster_centers_)
# The number of iterations required to converge
print(kmeans.n_iter_)
# first five predicted labels
print(kmeans.labels_[:5])
# init controls the initialization technique. The standard version of the k-means algorithm is implemented by setting init to "random". Setting this to "k-means++" employs an advanced trick to speed up convergence, which you’ll use later.
# n_clusters sets k for the clustering step. This is the most important parameter for k-means.
# n_init sets the number of initializations to perform. This is important because two runs can converge on different cluster assignments. The default behavior for the scikit-learn algorithm is to perform ten k-means runs and return the results of the one with the lowest SSE.
# max_iter sets the number of maximum iterations for each initialization of the k-means algorithm.Copyright © 2021 Codeinu
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