{"id":81235,"date":"2026-07-25T00:34:55","date_gmt":"2026-07-24T16:34:55","guid":{"rendered":"https:\/\/www.wsisp.com\/helps\/81235.html"},"modified":"2026-07-25T00:34:55","modified_gmt":"2026-07-24T16:34:55","slug":"03-kmeans-%e9%a1%be%e5%ae%a2%e6%95%b0%e6%8d%ae%e8%81%9a%e7%b1%bb%e5%88%86%e6%9e%90","status":"publish","type":"post","link":"https:\/\/www.wsisp.com\/helps\/81235.html","title":{"rendered":"03-Kmeans-\u987e\u5ba2\u6570\u636e\u805a\u7c7b\u5206\u6790"},"content":{"rendered":"

1. \u9700\u6c42\u5206\u6790<\/h2>\n

\u4f1a\u5458\u5ba2\u6237\u805a\u7c7b\u5206\u6790\u3002<\/p>\n

2. \u6570\u636e\u8bf4\u660e<\/h2>\n

\u8fd9\u662f Kaggle \u7ecf\u5178\u5ba2\u6237\u5206\u7fa4\u6559\u5b66\u6570\u636e\u96c6，\u5171 200 \u6761\u5546\u573a\u4f1a\u5458\u8131\u654f\u6837\u672c，\u4e13\u95e8\u7528\u4e8e\u65e0\u76d1\u7763\u805a\u7c7b（K-Means \u4e3a\u4e3b）\u5165\u95e8\u7ec3\u4e60，\u4e3a\u6a21\u62df\u5546\u4e1a\u6570\u636e，\u65e0\u771f\u5b9e\u7528\u6237\u9690\u79c1\u3002<\/p>\n

\u5546\u573a\u5ba2\u6237\u5206\u7fa4\u6570\u636e\u96c6\u6765\u6e90：Mall Customer Segmentation Data<\/p>\n\n\u5b57\u6bb5\u540d\u542b\u4e49\u6570\u636e\u7c7b\u578b<\/tr>\n\n\n\n\n\n\n
CustomerID<\/td>\n\u5ba2\u6237\u552f\u4e00\u7f16\u53f7<\/td>\n\u6574\u6570，\u4e3b\u952e<\/td>\n<\/tr>\n
Gender<\/td>\n\u6027\u522b（Male\/Female）<\/td>\n\u5206\u7c7b\u53d8\u91cf<\/td>\n<\/tr>\n
Age<\/td>\n\u5ba2\u6237\u5e74\u9f84<\/td>\n\u6570\u503c<\/td>\n<\/tr>\n
Annual Income (k$)<\/td>\n\u5e74\u6536\u5165（\u5355\u4f4d：\u5343\u7f8e\u5143）<\/td>\n\u6570\u503c<\/td>\n<\/tr>\n
Spending Score (1-100)<\/td>\n\u5546\u573a\u6d88\u8d39\u6253\u5206，1 \u6700\u4f4e\u3001100 \u6700\u9ad8，\u7531\u6d88\u8d39\u91d1\u989d \/ \u9891\u6b21\u7efc\u5408\u8ba1\u7b97<\/td>\n\u6838\u5fc3\u805a\u7c7b\u6570\u503c<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

\u6570\u636e\u7279\u5f81<\/p>\n

  • \u65e0\u7f3a\u5931\u503c\u3001\u65e0\u5f02\u5e38\u810f\u6570\u636e，\u6e05\u6d17\u6210\u672c\u6781\u4f4e；<\/li>\n
  • \u5305\u542b\u4eba\u53e3\u5c5e\u6027（\u6027\u522b\u3001\u5e74\u9f84）\u4e0e\u6d88\u8d39\u7ecf\u6d4e\u5c5e\u6027（\u6536\u5165\u3001\u6d88\u8d39\u5206）\u4e24\u7c7b\u7279\u5f81；<\/li>\n
  • \u6838\u5fc3\u5206\u6790\u7ef4\u5ea6：\u5e74\u6536\u5165 & \u6d88\u8d39\u5f97\u5206，\u4e8c\u8005\u5e38\u7528\u6765\u5212\u5206\u5178\u578b\u5ba2\u7fa4\u3002<\/li>\n

    3. \u5efa\u6a21<\/h2>\n

    import pandas as pd
    \nfrom matplotlib import pyplot as plt
    \nfrom sklearn.cluster import KMeans
    \nfrom sklearn.metrics import silhouette_score, calinski_harabasz_score <\/p>\n

    3.1 \u52a0\u8f7d\u6570\u636e<\/h3>\n

    # 1. \u83b7\u53d6\u6570\u636e
    \ndata = pd.read_csv('.\/data\/customers.csv')
    \ndata.head() <\/p>\n

    3.2 \u6570\u636e\u9884\u5904\u7406<\/h3>\n

    # 2. \u6570\u636e\u9884\u5904\u7406
    \n# 2.1 \u63d0\u53d6\u7279\u5f81
    \nx = data.iloc[:,3:5]
    \nx.head() <\/p>\n

    3.3 \u7279\u5f81\u5de5\u7a0b（\u65e0）<\/h3>\n

    3.4 \u6a21\u578b\u8bad\u7ec3<\/h3>\n

    \u786e\u5b9ak\u503c；\u8bad\u7ec3<\/p>\n

    # 4. \u6a21\u578b\u8bad\u7ec3
    \n# 4.1 \u786e\u5b9ak\u503c
    \n# \u5b9a\u4e49sse_list, sc_list, \u8bb0\u5f55\u4e0d\u540ck\u503c\u7684\u8bc4\u4f30\u6548\u679c
    \nsse_list = []
    \nsc_list = []<\/p>\n

    for k in range(2,20):
    \n estimator = KMeans(n_clusters=k, max_iter=100, random_state=1234)
    \n estimator.fit(x)
    \n y_pre = estimator.predict(x)
    \n sse_list.append(estimator.inertia_)
    \n sc_list.append(silhouette_score(x, y_pre))<\/p>\n

    # \u753b\u56fe（sse\u8098\u90e8\u56fe\u3001\u8f6e\u5ed3\u7cfb\u6570sc\u56fe）
    \n# \u4e24\u5f20\u56fe\u653e\u57281\u4e2a\u753b\u5e03\u4e2d<\/p>\n

    plt.figure(figsize=(6,3))
    \nplt.subplot(1,2,1)
    \nplt.plot(range(2,20), sse_list, marker='o', label='SSE')
    \nplt.grid()
    \nplt.title('SSE')<\/p>\n

    plt.subplot(1,2,2)
    \nplt.plot(range(2,20), sc_list, marker='o', label='SC')
    \nplt.grid()
    \nplt.title('SC')<\/p>\n

    plt.tight_layout()
    \nplt.show() <\/p>\n

    \"\"<\/p>\n

    # 4.2 \u6a21\u578b\u8bad\u7ec3
    \n# \u6839\u636esse\u8098\u90e8\u56fe\u548csc\u56fe，\u786e\u5b9ak\u503c=5
    \nestimator = KMeans(n_clusters=5, max_iter=100, random_state=1234)
    \nestimator.fit(x)
    \ny_pre = estimator.predict(x) <\/p>\n

    3.5 \u6a21\u578b\u8bc4\u4f30<\/h3>\n

    # 5. \u6a21\u578b\u8bc4\u4f30
    \nprint('sc\u6307\u6570:', silhouette_score(x, y_pre)) # [-1,1], \u8d8a\u63a5\u8fd11\u805a\u7c7b\u6548\u679c\u8d8a\u597d；>0.5\u805a\u7c7b\u826f\u597d；\u8d1f\u6570\u4e3a\u5927\u91cf\u6837\u672c\u9519\u5206，\u805a\u7c7b\u5931\u6548
    \nprint('CH\u6307\u6570:', calinski_harabasz_score(x, y_pre)) # [0,inf], \u8d8a\u63a5\u8fd1inf\u805a\u7c7b\u6548\u679c\u8d8a\u597d
    \nprint('SSE\u7684\u8d1f\u6570:', estimator.score(x)) # \u8d8a\u63a5\u8fd10\u805a\u7c7b\u6548\u679c\u8d8a\u597d
    \n# \u753b\u56fe
    \nplt.figure(figsize=(6,5))
    \nplt.scatter(x.iloc[:,0], x.iloc[:,1], c=y_pre)
    \nplt.xlabel(x.columns.tolist()[0])
    \nplt.ylabel(x.columns.tolist()[1])
    \nplt.scatter(estimator.cluster_centers_[:,0], estimator.cluster_centers_[:,1], marker='*', s=200, c='r')
    \nplt.title('K-Means')
    \nplt.show() <\/p>\n

    \"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

    1. \u9700\u6c42\u5206\u6790
    \n\u4f1a\u5458\u5ba2\u6237\u805a\u7c7b\u5206\u6790\u3002
    \n2. \u6570\u636e\u8bf4\u660e
    \n\u8fd9\u662f Kaggle \u7ecf\u5178\u5ba2\u6237\u5206\u7fa4\u6559\u5b66\u6570\u636e\u96c6，\u5171 200 \u6761\u5546\u573a\u4f1a\u5458\u8131\u654f\u6837\u672c，\u4e13\u95e8\u7528\u4e8e\u65e0\u76d1\u7763\u805a\u7c7b（K-Means \u4e3a\u4e3b）\u5165\u95e8\u7ec3\u4e60，\u4e3a\u6a21\u62df\u5546\u4e1a\u6570\u636e，\u65e0\u771f\u5b9e\u7528\u6237\u9690\u79c1\u3002
    \n\u5546\u573a\u5ba2\u6237\u5206\u7fa4\u6570\u636e\u96c6\u6765\u6e90：Mall Customer Segmentation Data
    \n\u5b57\u6bb5\u540d\u542b\u4e49\u6570\u636e\u7c7b\u578bCustomerID\u5ba2\u6237\u552f\u4e00\u7f16\u53f7\u6574\u6570，<\/p>\n","protected":false},"author":2,"featured_media":81233,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[5296,207,427],"topic":[],"class_list":["post-81235","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-server","tag-kmeans","tag-207","tag-427"],"yoast_head":"\n03-Kmeans-\u987e\u5ba2\u6570\u636e\u805a\u7c7b\u5206\u6790 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.wsisp.com\/helps\/81235.html\" \/>\n<meta property=\"og:locale\" content=\"zh_CN\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"03-Kmeans-\u987e\u5ba2\u6570\u636e\u805a\u7c7b\u5206\u6790 - \u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"og:description\" content=\"1. \u9700\u6c42\u5206\u6790 \u4f1a\u5458\u5ba2\u6237\u805a\u7c7b\u5206\u6790\u3002 2. \u6570\u636e\u8bf4\u660e \u8fd9\u662f Kaggle \u7ecf\u5178\u5ba2\u6237\u5206\u7fa4\u6559\u5b66\u6570\u636e\u96c6，\u5171 200 \u6761\u5546\u573a\u4f1a\u5458\u8131\u654f\u6837\u672c，\u4e13\u95e8\u7528\u4e8e\u65e0\u76d1\u7763\u805a\u7c7b（K-Means \u4e3a\u4e3b）\u5165\u95e8\u7ec3\u4e60，\u4e3a\u6a21\u62df\u5546\u4e1a\u6570\u636e，\u65e0\u771f\u5b9e\u7528\u6237\u9690\u79c1\u3002 \u5546\u573a\u5ba2\u6237\u5206\u7fa4\u6570\u636e\u96c6\u6765\u6e90：Mall Customer Segmentation Data \u5b57\u6bb5\u540d\u542b\u4e49\u6570\u636e\u7c7b\u578bCustomerID\u5ba2\u6237\u552f\u4e00\u7f16\u53f7\u6574\u6570，\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.wsisp.com\/helps\/81235.html\" \/>\n<meta property=\"og:site_name\" content=\"\u7f51\u7855\u4e92\u8054\u5e2e\u52a9\u4e2d\u5fc3\" \/>\n<meta property=\"article:published_time\" content=\"2026-07-24T16:34:55+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/www.wsisp.com\/helps\/wp-content\/uploads\/2026\/07\/20260724163454-6a63942e5f1bd.png\" \/>\n<meta name=\"author\" content=\"admin\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:label1\" content=\"\u4f5c\u8005\" \/>\n\t<meta name=\"twitter:data1\" content=\"admin\" \/>\n\t<meta name=\"twitter:label2\" content=\"\u9884\u8ba1\u9605\u8bfb\u65f6\u95f4\" \/>\n\t<meta name=\"twitter:data2\" content=\"1 \u5206\" \/>\n<script type=\"application\/ld+json\" class=\"yoast-schema-graph\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"WebPage\",\"@id\":\"https:\/\/www.wsisp.com\/helps\/81235.html\",\"url\":\"https:\/\/www.wsisp.com\/helps\/81235.html\",\"name\":\"03-Kmeans-\u987e\u5ba2\u6570\u636e\u805a\u7c7b\u5206\u6790 - 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