聚类

最新推荐文章于 2024-09-07 23:03:20 发布

翻译最新推荐文章于 2024-09-07 23:03:20 发布 · 529 阅读

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原文链接：https://scikit-learn.org/stable/modules/clustering.html

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#聚类 #python #机器学习 #聚类算法

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聚类

非标记数据的聚类在sklearn.cluster模块中执行。

聚类方法一览

在这里插入图片描述

K-means

KMeans算法通过最小化聚类间的距离平方和作为聚类标准。
$\sum\limits_{i=0}^n \min\limits_{\mu_{j} \in C}(||x_i - \mu_j||^2)$

KMeans算法等价于特殊情况下的期望最大算法。

# coding: utf-8
# Demonstration of k-means assumptions

import numpy as np
import matplotlib.pyplot as plt

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

plt.figure(figsize=(12, 12))

n_samples = 1500
random_state = 170
X, y = make_blobs(n_samples=n_samples, random_state=random_state)

y_pred = KMeans(n_clusters=2, random_state=random_state).fit_predict(X)

plt.subplot(221)
plt.scatter(X[:,0], X[:, 1], c=y_pred)
plt.title("Incorrect Number of Blobs")

transformation = [[0.60834549, -0.63667341], [-0.40887718, 0.85253229]]
X_aniso = np.dot(X, transformation)
y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_aniso)

plt.subplot(222)
plt.scatter(X_aniso[:,0], X_aniso[:, 1], c=y_pred)
plt.title("Anisotropicly Distributed Blobs")

X_varied, y_varied = make_blobs(n_samples=n_samples,
                                cluster_std=[1.0, 2.5, 0.5],
                                random_state=random_state)
y_pred = KMeans(n_clusters=3, random_state=random_state).fit_predict(X_varied)

plt.subplot(223)
plt.scatter(X_varied[:, 0], X_varied[:, 1], c=y_pred)
plt.title("Unequal Variance")

X_filtered = np.vstack((X[y == 0][:500], X[y == 1][:100], X[y == 2][:10]))
y_pred = KMeans(n_clusters=3,
                random_state=random_state).fit_predict(X_filtered)

plt.subplot(224)
plt.scatter(X_filtered[:, 0], X_filtered[:, 1], c=y_pred)
plt.title("Unevenly Sized Blobs")

plt.show()