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KMeans

Cruel Cat answered on June 30, 2021 Popularity 6/10 Helpfulness 2/10

answer KMeans

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KMeans

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from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=8)

Popularity 6/10 Helpfulness 2/10 Language python

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Tags: k-means python

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Contributed on Jun 30 2021

Cruel Cat

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import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.datasets import make_blobs
from sklearn.cluster import KMeans
X, y = make_blobs(n_samples=300, centers=4, cluster_std=0.60, random_state=0)
plt.scatter(X[:,0], X[:,1])
wcss = []
for i in range(1, 11):
    kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
    kmeans.fit(X)
    wcss.append(kmeans.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('Elbow Method')
plt.xlabel('Number of clusters')
plt.ylabel('WCSS')
plt.show()
kmeans = KMeans(n_clusters=4, init='k-means++', max_iter=300, n_init=10, random_state=0)
pred_y = kmeans.fit_predict(X)
plt.scatter(X[:,0], X[:,1])
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], s=300, c='red')
plt.show()

Popularity 3/10 Helpfulness 1/10 Language python

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Tags: k-means python

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Contributed on Nov 29 2022

Old-fashioned Okapi

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