{"id":1070289,"date":"2025-01-08T10:56:51","date_gmt":"2025-01-08T02:56:51","guid":{"rendered":"https:\/\/docs.pingcode.com\/ask\/ask-ask\/1070289.html"},"modified":"2025-01-08T10:56:53","modified_gmt":"2025-01-08T02:56:53","slug":"python-%e5%a6%82%e4%bd%95%e8%bf%9b%e8%a1%8c%e5%a4%9a%e7%bb%b4%e6%95%b0%e6%8d%ae%e8%81%9a%e7%b1%bb-2","status":"publish","type":"post","link":"https:\/\/docs.pingcode.com\/ask\/1070289.html","title":{"rendered":"python \u5982\u4f55\u8fdb\u884c\u591a\u7ef4\u6570\u636e\u805a\u7c7b"},"content":{"rendered":"

\"python<\/p>\n

Python\u8fdb\u884c\u591a\u7ef4\u6570\u636e\u805a\u7c7b\u7684\u65b9\u6cd5\u5305\u62ec\uff1a\u4f7f\u7528K-means\u7b97\u6cd5\u3001\u5c42\u6b21\u805a\u7c7b\u3001DBSCAN\u7b97\u6cd5\u3001Gaussian Mixture Models (GMM)<\/strong>\u7b49\u3002\u4e0b\u9762\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5176\u4e2d\u4e00\u79cd\u5e38\u7528\u7684\u805a\u7c7b\u7b97\u6cd5\u2014\u2014K-means\u7b97\u6cd5\u3002<\/p>\n<\/p>\n

K-means\u7b97\u6cd5\u662f\u4e00\u79cd\u8fed\u4ee3\u7b97\u6cd5\uff0c\u65e8\u5728\u5c06\u6570\u636e\u96c6\u5206\u6210K\u4e2a\u7c07\uff0c\u6bcf\u4e2a\u7c07\u5185\u90e8\u7684\u5143\u7d20\u5c3d\u91cf\u76f8\u4f3c\uff0c\u800c\u4e0d\u540c\u7c07\u4e4b\u95f4\u7684\u5143\u7d20\u5c3d\u91cf\u4e0d\u540c\u3002K-means\u7b97\u6cd5\u7684\u4e3b\u8981\u6b65\u9aa4\u5305\u62ec\uff1a\u521d\u59cb\u5316K\u4e2a\u7c31\u4e2d\u5fc3\uff0c\u5206\u914d\u6bcf\u4e2a\u6570\u636e\u70b9\u5230\u6700\u8fd1\u7684\u7c07\u4e2d\u5fc3\uff0c\u6839\u636e\u6570\u636e\u70b9\u7684\u5206\u914d\u66f4\u65b0\u7c07\u4e2d\u5fc3\uff0c\u91cd\u590d\u4e0a\u8ff0\u6b65\u9aa4\u76f4\u5230\u7c07\u4e2d\u5fc3\u4e0d\u518d\u53d8\u5316\u6216\u8fbe\u5230\u6700\u5927\u8fed\u4ee3\u6b21\u6570\u3002K-means\u7b97\u6cd5\u7684\u4f18\u70b9\u662f\u7b80\u5355\u9ad8\u6548\uff0c\u9002\u7528\u4e8e\u5927\u89c4\u6a21\u6570\u636e\u96c6\uff0c\u4f46\u9700\u8981\u9884\u5148\u6307\u5b9a\u7c07\u7684\u6570\u91cf\uff0c\u5bf9\u521d\u59cb\u7c07\u4e2d\u5fc3\u7684\u9009\u62e9\u8f83\u4e3a\u654f\u611f\u3002<\/p>\n<\/p>\n

\u63a5\u4e0b\u6765\uff0c\u5c06\u8be6\u7ec6\u4ecb\u7ecd\u5982\u4f55\u4f7f\u7528Python\u5b9e\u73b0\u591a\u7ef4\u6570\u636e\u805a\u7c7b\uff0c\u5e76\u4ee5K-means\u7b97\u6cd5\u4e3a\u4f8b\u8fdb\u884c\u6f14\u793a\u3002<\/p>\n<\/p>\n

\u4e00\u3001\u6570\u636e\u51c6\u5907<\/h3>\n<\/p>\n

\u5728\u8fdb\u884c\u6570\u636e\u805a\u7c7b\u4e4b\u524d\uff0c\u9996\u5148\u9700\u8981\u51c6\u5907\u6570\u636e\u3002\u53ef\u4ee5\u4f7f\u7528Python\u7684pandas\u5e93\u8bfb\u53d6\u6570\u636e\uff0c\u5e76\u5bf9\u6570\u636e\u8fdb\u884c\u9884\u5904\u7406\u3002\u4ee5\u4e0b\u662f\u4e00\u4e2a\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n

import pandas as pd<\/p>\n
from sklearn.preprocessing import StandardScaler<\/p>\n
\u8bfb\u53d6\u6570\u636e<\/strong><\/h2>\n
data = pd.read_csv('data.csv')<\/p>\n
\u6570\u636e\u9884\u5904\u7406<\/strong><\/h2>\n
scaler = StandardScaler()<\/p>\n
data_scaled = scaler.fit_transform(data)<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e8c\u3001\u4f7f\u7528K-means\u7b97\u6cd5\u8fdb\u884c\u805a\u7c7b<\/h3>\n<\/p>\n
K-means\u7b97\u6cd5\u662f\u6700\u5e38\u7528\u7684\u805a\u7c7b\u7b97\u6cd5\u4e4b\u4e00\uff0c\u4ee5\u4e0b\u662f\u4f7f\u7528K-means\u7b97\u6cd5\u8fdb\u884c\u805a\u7c7b\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
from sklearn.cluster import KMeans<\/p>\n
import matplotlib.pyplot as plt<\/p>\n
import seaborn as sns<\/p>\n
\u8bbe\u7f6e\u7c07\u7684\u6570\u91cf<\/strong><\/h2>\n
k = 3<\/p>\n
\u521d\u59cb\u5316K-means\u7b97\u6cd5<\/strong><\/h2>\n
kmeans = KMeans(n_clusters=k, random_state=42)<\/p>\n
\u8fdb\u884c\u805a\u7c7b<\/strong><\/h2>\n
kmeans.fit(data_scaled)<\/p>\n
\u83b7\u53d6\u805a\u7c7b\u7ed3\u679c<\/strong><\/h2>\n
labels = kmeans.labels_<\/p>\n
\u53ef\u89c6\u5316\u805a\u7c7b\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(10, 8))<\/p>\n
sns.scatterplot(x=data_scaled[:, 0], y=data_scaled[:, 1], hue=labels, palette='viridis')<\/p>\n
plt.title('K-means Clustering')<\/p>\n
plt.xlabel('Feature 1')<\/p>\n
plt.ylabel('Feature 2')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e09\u3001\u5c42\u6b21\u805a\u7c7b<\/h3>\n<\/p>\n
\u5c42\u6b21\u805a\u7c7b\u662f\u4e00\u79cd\u57fa\u4e8e\u6811\u7ed3\u6784\u7684\u805a\u7c7b\u7b97\u6cd5\uff0c\u53ef\u4ee5\u751f\u6210\u4e00\u4e2a\u5d4c\u5957\u7684\u805a\u7c7b\u5c42\u6b21\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528\u5c42\u6b21\u805a\u7c7b\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
from scipy.cluster.hierarchy import dendrogram, linkage<\/p>\n
\u8fdb\u884c\u5c42\u6b21\u805a\u7c7b<\/strong><\/h2>\n
linked = linkage(data_scaled, 'ward')<\/p>\n
\u7ed8\u5236\u6811\u72b6\u56fe<\/strong><\/h2>\n
plt.figure(figsize=(10, 8))<\/p>\n
dendrogram(linked, orientation='top', distance_sort='descending', show_leaf_counts=True)<\/p>\n
plt.title('Hierarchical Clustering Dendrogram')<\/p>\n
plt.xlabel('Sample Index')<\/p>\n
plt.ylabel('Distance')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u56db\u3001\u4f7f\u7528DBSCAN\u7b97\u6cd5\u8fdb\u884c\u805a\u7c7b<\/h3>\n<\/p>\n
DBSCAN\u7b97\u6cd5\u662f\u4e00\u79cd\u57fa\u4e8e\u5bc6\u5ea6\u7684\u805a\u7c7b\u7b97\u6cd5\uff0c\u80fd\u591f\u8bc6\u522b\u4efb\u610f\u5f62\u72b6\u7684\u7c07\uff0c\u5e76\u4e14\u5bf9\u566a\u58f0\u6570\u636e\u6709\u8f83\u597d\u7684\u9c81\u68d2\u6027\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528DBSCAN\u7b97\u6cd5\u8fdb\u884c\u805a\u7c7b\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
from sklearn.cluster import DBSCAN<\/p>\n
\u521d\u59cb\u5316DBSCAN\u7b97\u6cd5<\/strong><\/h2>\n
dbscan = DBSCAN(eps=0.5, min_samples=5)<\/p>\n
\u8fdb\u884c\u805a\u7c7b<\/strong><\/h2>\n
dbscan.fit(data_scaled)<\/p>\n
\u83b7\u53d6\u805a\u7c7b\u7ed3\u679c<\/strong><\/h2>\n
labels = dbscan.labels_<\/p>\n
\u53ef\u89c6\u5316\u805a\u7c7b\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(10, 8))<\/p>\n
sns.scatterplot(x=data_scaled[:, 0], y=data_scaled[:, 1], hue=labels, palette='viridis')<\/p>\n
plt.title('DBSCAN Clustering')<\/p>\n
plt.xlabel('Feature 1')<\/p>\n
plt.ylabel('Feature 2')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u4e94\u3001\u4f7f\u7528Gaussian Mixture Models (GMM)\u8fdb\u884c\u805a\u7c7b<\/h3>\n<\/p>\n
GMM\u662f\u4e00\u79cd\u57fa\u4e8e\u6982\u7387\u6a21\u578b\u7684\u805a\u7c7b\u7b97\u6cd5\uff0c\u53ef\u4ee5\u770b\u4f5c\u662fK-means\u7b97\u6cd5\u7684\u6269\u5c55\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528GMM\u8fdb\u884c\u805a\u7c7b\u7684\u6b65\u9aa4\uff1a<\/p>\n<\/p>\n
from sklearn.mixture import GaussianMixture<\/p>\n
\u8bbe\u7f6e\u7c07\u7684\u6570\u91cf<\/strong><\/h2>\n
k = 3<\/p>\n
\u521d\u59cb\u5316GMM\u7b97\u6cd5<\/strong><\/h2>\n
gmm = GaussianMixture(n_components=k, random_state=42)<\/p>\n
\u8fdb\u884c\u805a\u7c7b<\/strong><\/h2>\n
gmm.fit(data_scaled)<\/p>\n
\u83b7\u53d6\u805a\u7c7b\u7ed3\u679c<\/strong><\/h2>\n
labels = gmm.predict(data_scaled)<\/p>\n
\u53ef\u89c6\u5316\u805a\u7c7b\u7ed3\u679c<\/strong><\/h2>\n
plt.figure(figsize=(10, 8))<\/p>\n
sns.scatterplot(x=data_scaled[:, 0], y=data_scaled[:, 1], hue=labels, palette='viridis')<\/p>\n
plt.title('Gaussian Mixture Models Clustering')<\/p>\n
plt.xlabel('Feature 1')<\/p>\n
plt.ylabel('Feature 2')<\/p>\n
plt.show()<\/p>\n
<\/code><\/pre>\n<\/p>\n
\u516d\u3001\u9009\u62e9\u5408\u9002\u7684\u805a\u7c7b\u7b97\u6cd5<\/h3>\n<\/p>\n
\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u9009\u62e9\u5408\u9002\u7684\u805a\u7c7b\u7b97\u6cd5\u975e\u5e38\u91cd\u8981\u3002\u4e0d\u540c\u7684\u805a\u7c7b\u7b97\u6cd5\u9002\u7528\u4e8e\u4e0d\u540c\u7684\u6570\u636e\u5206\u5e03\u548c\u5e94\u7528\u573a\u666f\u3002\u4ee5\u4e0b\u662f\u51e0\u79cd\u5e38\u89c1\u805a\u7c7b\u7b97\u6cd5\u7684\u7279\u70b9\uff1a<\/p>\n<\/p>\n
    \n
  • K-means\u7b97\u6cd5<\/strong>\uff1a\u9002\u7528\u4e8e\u7c07\u5f62\u72b6\u4e3a\u51f8\u7684\u60c5\u51b5\uff0c\u5bf9\u566a\u58f0\u548c\u5f02\u5e38\u503c\u8f83\u654f\u611f\uff0c\u9700\u8981\u9884\u5148\u6307\u5b9a\u7c07\u7684\u6570\u91cf\u3002<\/li>\n
  • \u5c42\u6b21\u805a\u7c7b<\/strong>\uff1a\u4e0d\u9700\u8981\u9884\u5148\u6307\u5b9a\u7c07\u7684\u6570\u91cf\uff0c\u53ef\u4ee5\u751f\u6210\u5d4c\u5957\u7684\u805a\u7c7b\u5c42\u6b21\uff0c\u9002\u7528\u4e8e\u5c0f\u89c4\u6a21\u6570\u636e\u96c6\u3002<\/li>\n
  • DBSCAN\u7b97\u6cd5<\/strong>\uff1a\u9002\u7528\u4e8e\u4efb\u610f\u5f62\u72b6\u7684\u7c07\uff0c\u5bf9\u566a\u58f0\u548c\u5f02\u5e38\u503c\u5177\u6709\u8f83\u597d\u7684\u9c81\u68d2\u6027\uff0c\u4e0d\u9700\u8981\u9884\u5148\u6307\u5b9a\u7c07\u7684\u6570\u91cf\u3002<\/li>\n
  • GMM<\/strong>\uff1a\u57fa\u4e8e\u6982\u7387\u6a21\u578b\uff0c\u9002\u7528\u4e8e\u7c07\u5f62\u72b6\u4e3a\u9ad8\u65af\u5206\u5e03\u7684\u60c5\u51b5\uff0c\u53ef\u4ee5\u63d0\u4f9b\u7c07\u7684\u6982\u7387\u5206\u5e03\u3002<\/li>\n<\/ul>\n
    \u4e03\u3001\u8bc4\u4f30\u805a\u7c7b\u6548\u679c<\/h3>\n<\/p>\n
    \u8bc4\u4f30\u805a\u7c7b\u6548\u679c\u53ef\u4ee5\u4f7f\u7528\u591a\u79cd\u6307\u6807\uff0c\u4f8b\u5982\u8f6e\u5ed3\u7cfb\u6570\uff08Silhouette Score\uff09\u3001\u8c03\u6574\u5170\u5fb7\u6307\u6570\uff08Adjusted Rand Index\uff09\u3001\u5f52\u4e00\u5316\u4e92\u4fe1\u606f\uff08Normalized Mutual Information\uff09\u7b49\u3002\u4ee5\u4e0b\u662f\u4f7f\u7528\u8f6e\u5ed3\u7cfb\u6570\u8bc4\u4f30\u805a\u7c7b\u6548\u679c\u7684\u793a\u4f8b\u4ee3\u7801\uff1a<\/p>\n<\/p>\n
    from sklearn.metrics import silhouette_score<\/p>\n
    \u8ba1\u7b97\u8f6e\u5ed3\u7cfb\u6570<\/strong><\/h2>\n
    score = silhouette_score(data_scaled, labels)<\/p>\n
    print(f'Silhouette Score: {score}')<\/p>\n
    <\/code><\/pre>\n<\/p>\n
    \u516b\u3001\u603b\u7ed3<\/h3>\n<\/p>\n
    \u672c\u6587\u4ecb\u7ecd\u4e86Python\u8fdb\u884c\u591a\u7ef4\u6570\u636e\u805a\u7c7b\u7684\u51e0\u79cd\u5e38\u7528\u65b9\u6cd5\uff0c\u5305\u62ecK-means\u7b97\u6cd5\u3001\u5c42\u6b21\u805a\u7c7b\u3001DBSCAN\u7b97\u6cd5\u3001Gaussian Mixture Models (GMM)\u7b49\uff0c\u5e76\u8be6\u7ec6\u8bb2\u89e3\u4e86\u5982\u4f55\u4f7f\u7528\u8fd9\u4e9b\u7b97\u6cd5\u8fdb\u884c\u805a\u7c7b\u3002\u9009\u62e9\u5408\u9002\u7684\u805a\u7c7b\u7b97\u6cd5\u548c\u8bc4\u4f30\u805a\u7c7b\u6548\u679c\u662f\u786e\u4fdd\u805a\u7c7b\u7ed3\u679c\u6709\u6548\u7684\u91cd\u8981\u6b65\u9aa4\u3002\u5728\u5b9e\u9645\u5e94\u7528\u4e2d\uff0c\u53ef\u4ee5\u6839\u636e\u6570\u636e\u7684\u7279\u70b9\u548c\u5177\u4f53\u9700\u6c42\uff0c\u9009\u62e9\u5408\u9002\u7684\u805a\u7c7b\u7b97\u6cd5\uff0c\u4ee5\u83b7\u5f97\u6700\u4f73\u7684\u805a\u7c7b\u6548\u679c\u3002<\/p>\n<\/p>\n
    \u76f8\u5173\u95ee\u7b54FAQs\uff1a<\/strong><\/h2>\n
     \u4ec0\u4e48\u662f\u591a\u7ef4\u6570\u636e\u805a\u7c7b\uff0c\u4e3a\u4ec0\u4e48\u5728\u6570\u636e\u5206\u6790\u4e2d\u91cd\u8981\uff1f<\/strong>
    \u591a\u7ef4\u6570\u636e\u805a\u7c7b\u662f\u5c06\u591a\u4e2a\u7279\u5f81\uff08\u7ef4\u5ea6\uff09\u7684\u6570\u636e\u70b9\u5206\u7ec4\u7684\u8fc7\u7a0b\uff0c\u76ee\u7684\u662f\u4f7f\u540c\u4e00\u7ec4\u4e2d\u7684\u6570\u636e\u70b9\u5728\u7279\u5f81\u4e0a\u66f4\u52a0\u76f8\u4f3c\uff0c\u800c\u4e0d\u540c\u7ec4\u7684\u6570\u636e\u70b9\u4e4b\u95f4\u5dee\u5f02\u66f4\u5927\u3002\u8fd9\u4e00\u8fc7\u7a0b\u5728\u6570\u636e\u5206\u6790\u4e2d\u81f3\u5173\u91cd\u8981\uff0c\u56e0\u4e3a\u5b83\u53ef\u4ee5\u5e2e\u52a9\u8bc6\u522b\u6f5c\u5728\u7684\u6a21\u5f0f\u548c\u8d8b\u52bf\uff0c\u53d1\u73b0\u6570\u636e\u4e2d\u7684\u7c7b\u522b\u7ed3\u6784\uff0c\u4ece\u800c\u4e3a\u51b3\u7b56\u63d0\u4f9b\u652f\u6301\u3002<\/p>\n
    \u5728Python\u4e2d\u6709\u54ea\u4e9b\u5e38\u7528\u7684\u5e93\u53ef\u4ee5\u8fdb\u884c\u591a\u7ef4\u6570\u636e\u805a\u7c7b\uff1f<\/strong>
    \u5728Python\u4e2d\uff0c\u5e38\u7528\u7684\u5e93\u5305\u62ecScikit-learn\u3001SciPy\u548cK-means\u7b49\u3002Scikit-learn\u63d0\u4f9b\u4e86\u591a\u79cd\u805a\u7c7b\u7b97\u6cd5\uff0c\u5982K-means\u3001\u5c42\u6b21\u805a\u7c7b\u548cDBSCAN\uff0c\u975e\u5e38\u9002\u5408\u5904\u7406\u591a\u7ef4\u6570\u636e\u3002SciPy\u5219\u63d0\u4f9b\u4e86\u66f4\u5e95\u5c42\u7684\u5de5\u5177\u548c\u51fd\u6570\uff0c\u9002\u5408\u9700\u8981\u81ea\u5b9a\u4e49\u805a\u7c7b\u7b97\u6cd5\u7684\u7528\u6237\u3002<\/p>\n
    \u5982\u4f55\u8bc4\u4f30\u805a\u7c7b\u7684\u6548\u679c\uff1f<\/strong>
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