{"id":86553,"date":"2024-03-21T07:10:25","date_gmt":"2024-03-20T22:10:25","guid":{"rendered":"https:\/\/www.silicloud.com\/ja\/blog\/python-%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e3%80%81%e8%a4%87%e6%95%b0%e3%81%ae%e7%89%b9%e6%80%a7%e3%81%ab%e5%9f%ba%e3%81%a5%e3%81%84%e3%81%a6%e3%82%af%e3%83%a9%e3%82%b9%e3%82%bf%e3%83%bc\/"},"modified":"2024-04-04T12:24:52","modified_gmt":"2024-04-04T03:24:52","slug":"python-%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e3%80%81%e8%a4%87%e6%95%b0%e3%81%ae%e7%89%b9%e6%80%a7%e3%81%ab%e5%9f%ba%e3%81%a5%e3%81%84%e3%81%a6%e3%82%af%e3%83%a9%e3%82%b9%e3%82%bf%e3%83%bc","status":"publish","type":"post","link":"https:\/\/www.silicloud.com\/ja\/blog\/python-%e3%82%92%e4%bd%bf%e7%94%a8%e3%81%97%e3%81%a6%e3%80%81%e8%a4%87%e6%95%b0%e3%81%ae%e7%89%b9%e6%80%a7%e3%81%ab%e5%9f%ba%e3%81%a5%e3%81%84%e3%81%a6%e3%82%af%e3%83%a9%e3%82%b9%e3%82%bf%e3%83%bc\/","title":{"rendered":"Python \u3092\u4f7f\u7528\u3057\u3066\u3001\u8907\u6570\u306e\u7279\u6027\u306b\u57fa\u3065\u3044\u3066\u30af\u30e9\u30b9\u30bf\u30fc\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5"},"content":{"rendered":"

scikit-learn\u30e9\u30a4\u30d6\u30e9\u30ea\u306eKMeans\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001Python\u3067\u30de\u30eb\u30c1\u5c5e\u6027\u3092\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3067\u304d\u307e\u3059\u3002<\/p>\n

\u591a\u5c5e\u6027\u3092\u30af\u30e9\u30b9\u30bf\u5206\u6790\u3059\u308b\u624b\u9806\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002<\/p>\n

    \n
  1. \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b\uff1a<\/li>\n
  2. from sklearn.cluster import KMeans
    \nimport pandas as pd
    \n# \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u8aad\u307f\u8fbc\u307f
    \ndataset = pd.read_csv(‘dataset.csv’)<\/li>\n
  3. \u30c7\u30fc\u30bf\u306e\u524d\u51e6\u7406:<\/li>\n
  4. \u591a\u5c5e\u6027\u306e\u30af\u30e9\u30b9\u30bf\u5206\u6790\u3067\u306f\u3001\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u5185\u306e\u5404\u5c5e\u6027\u3092\u6b63\u898f\u5316\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002\u3053\u308c\u306b\u3088\u308a\u3001\u3059\u3079\u3066\u306e\u5c5e\u6027\u304c\u540c\u3058\u91cd\u8981\u5ea6\u3092\u6301\u3064\u3088\u3046\u306b\u306a\u308a\u307e\u3059\u3002<\/li>\n
  5. sklearn.preprocessing \u304b\u3089 StandardScaler \u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b# \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u6a19\u6e96\u5316
    \nscaler = StandardScaler()
    \nscaled_dataset = scaler.fit_transform(dataset)<\/li>\n
  6. KMeans\u6cd5\u306b\u3088\u308b\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u5206\u6790\u3092\u884c\u3046<\/li>\n
  7. Elbow\u6cd5\u3092\u4f7f\u7528\u3057\u3066\u3001\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306e\u6700\u9069\u306a\u6570\u3092\u6c7a\u5b9a\u3057\u3066\u304b\u3089\u3001\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u5fc5\u8981\u304c\u3042\u308a\u307e\u3059\u3002<\/li>\n
  8. \u30af\u30e9\u30b9\u30bf\u306e\u6700\u9069\u306a\u6570\u3092\u898b\u3064\u3051\u308b
    \nwcss = []
    \nfor i in range(1, 11):
    \nkmeans = KMeans(n_clusters=i, init=’k-means++’, random_state=42)
    \nkmeans.fit(scaled_dataset)
    \nwcss.append(kmeans.inertia_)# Elbow\u6cd5\u306e\u30b0\u30e9\u30d5\u3092\u63cf\u753b\u3057\u307e\u3059
    \nimport matplotlib.pyplot as plt
    \nplt.plot(range(1, 11), wcss)
    \nplt.xlabel(‘\u30af\u30e9\u30b9\u30bf\u306e\u6570’)
    \nplt.ylabel(‘WCSS’)
    \nplt.show()<\/li>\n
  9. \u30a8\u30eb\u30dc\u30fc\u30e1\u30bd\u30c3\u30c9\u3092\u7528\u3044\u3066\u3001WCSS\u5024\u306e\u5909\u52d5\u304c\u7de9\u3084\u304b\u306b\u306a\u308b\u30af\u30e9\u30b9\u30bf\u30fc\u6570\u3092\u6c7a\u5b9a\u3059\u308b\u3002<\/li>\n
  10. \u30af\u30e9\u30b9\u30bf\u5206\u6790\u3092\u5b9f\u65bd\u3059\u308b<\/li>\n
  11. \u30af\u30e9\u30b9\u30bf\u6570\u56fa\u5b9a\u3067\u306e\u30af\u30e9\u30b9\u30bf\u5206\u6790<\/li>\n
  12. 3\u3064\u306e\u30af\u30e9\u30b9\u30bf\u6570\u3092\u7528\u3044\u305fKMeans++\u6cd5\u3092\u4f7f\u7528\u3057\u3066\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u5206\u6790\u3092\u884c\u3063\u305f<\/li>\n
  13. \u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u7d50\u679c\u306fclusters\u5909\u6570\u306b\u4fdd\u6301\u3055\u308c\u3001\u5404\u30b5\u30f3\u30d7\u30eb\u304c\u5c5e\u3059\u308b\u30af\u30e9\u30b9\u30bf\u3092\u793a\u3057\u307e\u3059\u3002<\/li>\n
  14. \u53ef\u8996\u5316\u3057\u305f\u30af\u30e9\u30b9\u30bf\u306e\u7d50\u679c\uff1a<\/li>\n
  15. \u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u306e\u7d50\u679c\u3092\u8996\u899a\u5316\u3059\u308b\u306b\u306f\u3001Matplotlib\u30e9\u30a4\u30d6\u30e9\u30ea\u3092\u4f7f\u7528\u3067\u304d\u307e\u3059\u3002<\/li>\n
  16. plt.scatter(\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8[‘\u5c5e\u60271’], \u30c7\u30fc\u30bf\u30bb\u30c3\u30c8[‘\u5c5e\u60272’], c=clusters)
    \nplt.xlabel(‘\u5c5e\u60271’)
    \nplt.ylabel(‘\u5c5e\u60272’)
    \nplt.show()<\/li>\n
  17. \u6563\u5e03\u56f3\u4e2d\u306e\u7570\u306a\u308b\u8272\u306e\u70b9\u306f\u3001\u7570\u306a\u308b\u30af\u30e9\u30b9\u30bf\u3092\u8868\u3057\u3066\u3044\u307e\u3059\u3002<\/li>\n<\/ol>\n

    \u4e0a\u8a18\u306f\u57fa\u672c\u7684\u306a\u591a\u5c5e\u6027\u30af\u30e9\u30b9\u30bf\u5206\u6790\u306e\u624b\u9806\u3067\u3042\u308a\u3001\u5177\u4f53\u7684\u306a\u30cb\u30fc\u30ba\u3084\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u306e\u7279\u6027\u306b\u5fdc\u3058\u3066\u5fc5\u8981\u306b\u5fdc\u3058\u3066\u8abf\u6574\u3084\u62e1\u5f35\u3092\u884c\u3046\u3053\u3068\u304c\u3067\u304d\u307e\u3059\u3002<\/p>\n","protected":false},"excerpt":{"rendered":"

    scikit-learn\u30e9\u30a4\u30d6\u30e9\u30ea\u306eKMeans\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001Python\u3067\u30de\u30eb\u30c1\u5c5e\u6027\u3092\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3067\u304d\u307e\u3059\u3002 \u591a\u5c5e\u6027\u3092\u30af\u30e9\u30b9\u30bf\u5206\u6790\u3059\u308b\u624b\u9806\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002 \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b […]<\/p>\n","protected":false},"author":4,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-86553","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"yoast_head":"\nPython \u3092\u4f7f\u7528\u3057\u3066\u3001\u8907\u6570\u306e\u7279\u6027\u306b\u57fa\u3065\u3044\u3066\u30af\u30e9\u30b9\u30bf\u30fc\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5 - Blog - Silicon Cloud<\/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.silicloud.com\/ja\/blog\/python-\u3092\u4f7f\u7528\u3057\u3066\u3001\u8907\u6570\u306e\u7279\u6027\u306b\u57fa\u3065\u3044\u3066\u30af\u30e9\u30b9\u30bf\u30fc\/\" \/>\n<meta property=\"og:locale\" content=\"ja_JP\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Python \u3092\u4f7f\u7528\u3057\u3066\u3001\u8907\u6570\u306e\u7279\u6027\u306b\u57fa\u3065\u3044\u3066\u30af\u30e9\u30b9\u30bf\u30fc\u5206\u6790\u3092\u5b9f\u884c\u3059\u308b\u65b9\u6cd5\" \/>\n<meta property=\"og:description\" content=\"scikit-learn\u30e9\u30a4\u30d6\u30e9\u30ea\u306eKMeans\u30a2\u30eb\u30b4\u30ea\u30ba\u30e0\u3092\u4f7f\u7528\u3059\u308b\u3068\u3001Python\u3067\u30de\u30eb\u30c1\u5c5e\u6027\u3092\u30af\u30e9\u30b9\u30bf\u30ea\u30f3\u30b0\u3067\u304d\u307e\u3059\u3002 \u591a\u5c5e\u6027\u3092\u30af\u30e9\u30b9\u30bf\u5206\u6790\u3059\u308b\u624b\u9806\u306f\u6b21\u306e\u3068\u304a\u308a\u3067\u3059\u3002 \u5fc5\u8981\u306a\u30e9\u30a4\u30d6\u30e9\u30ea\u3068\u30c7\u30fc\u30bf\u30bb\u30c3\u30c8\u3092\u30a4\u30f3\u30dd\u30fc\u30c8\u3059\u308b […]\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.silicloud.com\/ja\/blog\/python-\u3092\u4f7f\u7528\u3057\u3066\u3001\u8907\u6570\u306e\u7279\u6027\u306b\u57fa\u3065\u3044\u3066\u30af\u30e9\u30b9\u30bf\u30fc\/\" \/>\n<meta property=\"og:site_name\" content=\"Blog - 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