python实例：
import pandas as pd
df = pd.read_csv('./datasets/ch1ex1.csv')
points = df.values
df.head()
#散点图观察
import matplotlib.pyplot as plt
xs = points[:,0]
ys = points[:,1]
plt.scatter(xs, ys)
plt.show()
#聚类操作
df = pd.read_csv('./datasets/ch1ex1.csv')
points = df.values
from sklearn.cluster import KMeans
model = KMeans(n_clusters=3)
model.fit(points)
labels = model.predict(points)
labels
array([2, 0, 1, 1, 0, 0, 1, 2, 0, 0, 1, 2, 0, 1, 0, 2, 1, 1, 2, 1, 0, 2, 0,
2, 2, 0, 2, 2, 2, 0, 1, 1, 1, 0, 2, 0, 2, 2, 0, 2, 2, 1, 0, 0, 0, 2,
2, 1, 2, 1, 1, 1, 2, 2, 2, 0, 2, 2, 0, 1, 0, 2, 2, 1, 1, 0, 1, 0, 0,
2, 1, 0, 1, 2, 1, 0, 2, 2, 2, 1, 2, 0, 1, 0, 0, 0, 0, 2, 2, 1, 0, 1,
0, 2, 2, 2, 1, 0, 0, 1, 0, 2, 0, 1, 2, 1, 1, 1, 0, 0, 2, 0, 1, 0, 0,
0, 2, 0, 1, 1, 2, 2, 2, 2, 2, 0, 1, 2, 0, 0, 1, 1, 0, 2, 0, 2, 1, 0,
1, 2, 1, 1, 2, 1, 1, 2, 1, 0, 2, 2, 2, 1, 1, 0, 1, 0, 2, 2, 1, 0, 1,
1, 1, 0, 2, 2, 0, 1, 1, 2, 2, 1, 2, 2, 0, 2, 1, 1, 1, 2, 2, 1, 2, 1,
1, 2, 0, 1, 2, 2, 2, 2, 0, 1, 2, 0, 0, 0, 2, 0, 2, 2, 0, 1, 1, 2, 1,
2, 2, 0, 0, 2, 1, 0, 1, 2, 1, 0, 2, 0, 0, 0, 0, 1, 1, 1, 2, 2, 0, 2,
1, 0, 2, 2, 0, 2, 1, 1, 1, 1, 1, 0, 2, 2, 1, 1, 2, 0, 1, 0, 0, 2, 2,
0, 0, 0, 2, 1, 2, 0, 2, 1, 1, 1, 1, 1, 2, 2, 0, 2, 2, 0, 1, 1, 0, 2,
1, 1, 0, 0, 2, 2, 2, 0, 0, 2, 1, 0, 0, 1, 2, 2, 2, 0, 2, 2, 2, 0, 0,
0])
#聚类中心
centroids = model.cluster_centers_
centroids_x = centroids[:,0]
centroids_y = centroids[:,1]
plt.scatter(xs, ys, c=labels)
plt.scatter(centroids_x, centroids_y, marker='X', s=200)
plt.show()
K值对结果的影响
import pandas as pd
seeds_df = pd.read_csv('./datasets/seeds.csv')
varieties = list(seeds_df['grain_variety'])
del seeds_df['grain_variety']
seeds_df.head()
samples = seeds_df.values
from sklearn.cluster import KMeans
ks = range(1, 6)
inertias = []
for k in ks:
# Create a KMeans instance with k clusters: model
model = KMeans(n_clusters=k)
# Fit model to samples
model.fit(samples)
# Append the inertia to the list of inertias
inertias.append(model.inertia_)
import matplotlib.pyplot as plt
Plot ks vs inertias
plt.plot(ks, inertias, '-o')
plt.xlabel('number of clusters, k')
plt.ylabel('inertia')
plt.xticks(ks)
plt.show()
K-means的结果带有一定的随机性
model = KMeans(n_clusters=3)
labels = model.fit_predict(samples)
df = pd.DataFrame({'labels': labels, 'varieties': varieties})
ct = pd.crosstab(df['labels'], df['varieties'])
ct
make_pipeline更方便
import pandas as pd
df = pd.read_csv('./datasets/fish.csv')
species = list(df['species'])
del df['species']
df.head()
samples = df.values
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import KMeans
scaler = StandardScaler()
kmeans = KMeans(n_clusters=4)
pipeline = make_pipeline(scaler, kmeans)
pipeline.fit(samples)
labels = pipeline.predict(samples)
df = pd.DataFrame({'labels': labels, 'species': species})
ct = pd.crosstab(df['labels'], df['species'])
ct
Original: https://blog.csdn.net/weixin_53660567/article/details/122963458
Author: 长沙有肥鱼
Title: K-MEANS算法
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