{"id":1245,"date":"2023-07-27T04:03:40","date_gmt":"2023-07-27T04:03:40","guid":{"rendered":"https:\/\/statorials.org\/cn\/k-%e8%a1%a8%e7%a4%ba%e5%9c%a8-r-%e4%b8%ad%e5%88%86%e7%bb%84\/"},"modified":"2023-07-27T04:03:40","modified_gmt":"2023-07-27T04:03:40","slug":"k-%e8%a1%a8%e7%a4%ba%e5%9c%a8-r-%e4%b8%ad%e5%88%86%e7%bb%84","status":"publish","type":"post","link":"https:\/\/statorials.org\/cn\/k-%e8%a1%a8%e7%a4%ba%e5%9c%a8-r-%e4%b8%ad%e5%88%86%e7%bb%84\/","title":{"rendered":"R \u4e2d\u7684 k \u5747\u503c\u805a\u7c7b\uff1a\u5206\u6b65\u793a\u4f8b"},"content":{"rendered":"<p><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u805a\u7c7b\u662f\u4e00\u79cd\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u5c1d\u8bd5\u5728\u6570\u636e\u96c6\u4e2d\u67e5\u627e<a href=\"https:\/\/statorials.org\/cn\/\u7edf\u8ba1\u89c2\u5bdf\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u89c2\u5bdf<\/a><em>\u7ec4<\/em>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u76ee\u6807\u662f\u627e\u5230\u805a\u7c7b\uff0c\u4f7f\u5f97\u6bcf\u4e2a\u805a\u7c7b\u5185\u7684\u89c2\u5bdf\u7ed3\u679c\u5f7c\u6b64\u975e\u5e38\u76f8\u4f3c\uff0c\u800c\u4e0d\u540c\u805a\u7c7b\u4e2d\u7684\u89c2\u5bdf\u7ed3\u679c\u5f7c\u6b64\u975e\u5e38\u4e0d\u540c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u805a\u7c7b\u662f<a href=\"https:\/\/statorials.org\/cn\/\u76d1\u7763\u5b66\u4e60\u4e0e\u65e0\u76d1\u7763\u5b66\u4e60\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u65e0\u76d1\u7763\u5b66\u4e60<\/a>\u7684\u4e00\u79cd\u5f62\u5f0f\uff0c\u56e0\u4e3a\u6211\u4eec\u53ea\u662f\u8bd5\u56fe\u5728\u6570\u636e\u96c6\u4e2d\u627e\u5230\u7ed3\u6784\uff0c\u800c\u4e0d\u662f\u9884\u6d4b<a href=\"https:\/\/statorials.org\/cn\/\u53d8\u91cf\u89e3\u91ca\u6027\u53cd\u5e94\/\" target=\"_blank\" rel=\"noopener noreferrer\">\u54cd\u5e94\u53d8\u91cf<\/a>\u7684\u503c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5f53\u4f01\u4e1a\u53ef\u4ee5\u8bbf\u95ee\u4ee5\u4e0b\u4fe1\u606f\u65f6\uff0c\u805a\u7c7b\u901a\u5e38\u7528\u4e8e\u8425\u9500\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5bb6\u5ead\u6536\u5165<\/span><\/li>\n<li><span style=\"color: #000000;\">\u623f\u5b50\u5927\u5c0f<\/span><\/li>\n<li><span style=\"color: #000000;\">\u6237\u4e3b\u804c\u4e1a<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5230\u6700\u8fd1\u5e02\u533a\u7684\u8ddd\u79bb<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u5f53\u6b64\u4fe1\u606f\u53ef\u7528\u65f6\uff0c\u805a\u7c7b\u53ef\u7528\u4e8e\u8bc6\u522b\u76f8\u4f3c\u7684\u5bb6\u5ead\uff0c\u5e76\u4e14\u53ef\u80fd\u66f4\u6709\u53ef\u80fd\u8d2d\u4e70\u67d0\u4e9b\u4ea7\u54c1\u6216\u5bf9\u67d0\u79cd\u7c7b\u578b\u7684\u5e7f\u544a\u505a\u51fa\u66f4\u597d\u7684\u53cd\u5e94\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6700\u5e38\u89c1\u7684\u805a\u7c7b\u5f62\u5f0f\u4e4b\u4e00\u79f0\u4e3a<strong>k \u5747\u503c\u805a\u7c7b<\/strong>\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u4ec0\u4e48\u662f K \u5747\u503c\u805a\u7c7b\uff1f<\/strong><\/span><\/h3>\n<p> <span style=\"color: #000000;\">K \u5747\u503c\u805a\u7c7b\u662f\u4e00\u79cd\u5c06\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e2a\u89c2\u5bdf\u7ed3\u679c\u653e\u5165<em>K<\/em>\u4e2a\u805a\u7c7b\u4e2d\u7684\u4e00\u4e2a\u6280\u672f\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6700\u7ec8\u76ee\u6807\u662f\u62e5\u6709<em>K \u4e2a<\/em>\u7c07\uff0c\u5176\u4e2d\u6bcf\u4e2a\u7c07\u5185\u7684\u89c2\u5bdf\u7ed3\u679c\u5f7c\u6b64\u975e\u5e38\u76f8\u4f3c\uff0c\u800c\u4e0d\u540c\u7c07\u4e2d\u7684\u89c2\u5bdf\u7ed3\u679c\u5f7c\u6b64\u975e\u5e38\u4e0d\u540c\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5728\u5b9e\u8df5\u4e2d\uff0c\u6211\u4eec\u4f7f\u7528\u4ee5\u4e0b\u6b65\u9aa4\u6765\u8fdb\u884cK-means\u805a\u7c7b\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u9009\u62e9<em>K<\/em>\u503c\u3002<\/strong><\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u9700\u8981\u51b3\u5b9a\u8981\u5728\u6570\u636e\u4e2d\u8bc6\u522b\u591a\u5c11\u4e2a\u7c07\u3002\u901a\u5e38\u6211\u4eec\u53ea\u9700\u8981\u6d4b\u8bd5\u51e0\u4e2a\u4e0d\u540c\u7684<em>K<\/em>\u503c\u5e76\u5206\u6790\u7ed3\u679c\uff0c\u770b\u770b\u5bf9\u4e8e\u7ed9\u5b9a\u95ee\u9898\uff0c\u54ea\u4e2a\u7c07\u6570\u4f3c\u4e4e\u6700\u6709\u610f\u4e49\u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\"><strong>2. \u5c06\u6bcf\u4e2a\u89c2\u6d4b\u503c\u968f\u673a\u5206\u914d\u5230\u4e00\u4e2a\u521d\u59cb\u7c07\uff08\u4ece 1 \u5230<em>K\uff09<\/em> \u3002<\/strong><\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>3. \u6267\u884c\u4ee5\u4e0b\u8fc7\u7a0b\uff0c\u76f4\u5230\u96c6\u7fa4\u5206\u914d\u505c\u6b62\u66f4\u6539\u3002<\/strong><\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u5bf9\u4e8e\u6bcf\u4e2a<em>K<\/em>\u4e2a\u7c07\uff0c\u8ba1\u7b97<em>\u8be5\u7c07\u7684\u91cd\u5fc3\u3002<\/em>\u8fd9\u53ea\u662f<em>\u7b2c k \u4e2a<\/em>\u7c07\u7684\u89c2\u6d4b\u503c\u7684<em>p<\/em>\u5747\u503c\u7279\u5f81\u7684\u5411\u91cf\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5c06\u6bcf\u4e2a\u89c2\u6d4b\u503c\u5206\u914d\u7ed9\u5177\u6709\u6700\u8fd1\u8d28\u5fc3\u7684\u7c07\u3002\u8fd9\u91cc\uff0c<em>\u6700\u63a5\u8fd1<\/em>\u662f\u4f7f\u7528<a href=\"https:\/\/en.wikipedia.org\/wiki\/Euclidean_distance#Squared_Euclidean_distance\" target=\"_blank\" rel=\"noopener noreferrer\">\u6b27\u51e0\u91cc\u5fb7\u8ddd\u79bb<\/a>\u5b9a\u4e49\u7684\u3002<\/span><\/li>\n<\/ul>\n<h3> <span style=\"color: #000000;\"><strong>R \u4e2d\u7684 K \u5747\u503c\u805a\u7c7b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u6559\u7a0b\u63d0\u4f9b\u4e86\u5982\u4f55\u5728 R \u4e2d\u6267\u884c k \u5747\u503c\u805a\u7c7b\u7684\u5206\u6b65\u793a\u4f8b\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c1\u6b65\uff1a\u52a0\u8f7d\u5fc5\u8981\u7684\u5305<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u5c06\u52a0\u8f7d\u4e24\u4e2a\u5305\uff0c\u5176\u4e2d\u5305\u542b R \u4e2d k \u5747\u503c\u805a\u7c7b\u7684\u51e0\u4e2a\u6709\u7528\u51fd\u6570\u3002<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #993300;\">library<\/span> (factoextra)\n<span style=\"color: #993300;\">library<\/span> (cluster)<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 2 \u6b65\uff1a\u52a0\u8f7d\u548c\u51c6\u5907\u6570\u636e<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u5728\u672c\u4f8b\u4e2d\uff0c\u6211\u4eec\u5c06\u4f7f\u7528 R \u4e2d\u5185\u7f6e\u7684<em>USArrests<\/em>\u6570\u636e\u96c6\uff0c\u5176\u4e2d\u5305\u542b 1973 \u5e74\u7f8e\u56fd\u5404\u5dde\u6bcf 10 \u4e07\u4eba\u56e0<em>\u8c0b\u6740<\/em>\u3001<em>\u88ad\u51fb<\/em>\u548c<em>\u5f3a\u5978<\/em>\u800c\u88ab\u6355\u7684\u4eba\u6570\uff0c\u4ee5\u53ca\u6bcf\u4e2a\u5dde\u5c45\u4f4f\u5728\u57ce\u5e02\u7684\u4eba\u53e3\u767e\u5206\u6bd4\u5730\u533a\u3002 \u3001<em>\u90fd\u5e02\u6d41\u884c<\/em>\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u4ee5\u4e0b\u4ee3\u7801\u663e\u793a\u4e86\u5982\u4f55\u6267\u884c\u4ee5\u4e0b\u64cd\u4f5c\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u52a0\u8f7d<em>USArrests<\/em>\u6570\u636e\u96c6<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5220\u9664\u6240\u6709\u6709\u7f3a\u5931\u503c\u7684\u884c<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5c06\u6570\u636e\u96c6\u4e2d\u7684\u6bcf\u4e2a\u53d8\u91cf\u7f29\u653e\u4e3a\u5747\u503c 0 \u548c\u6807\u51c6\u5dee 1<\/span><\/li>\n<\/ul>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#load data<\/span>\ndf &lt;-USArrests\n\n<span style=\"color: #008080;\">#remove rows with missing values<\/span><\/strong>\n<strong>df &lt;- na. <span style=\"color: #3366ff;\">omitted<\/span> (df)\n\n<span style=\"color: #008080;\">#scale each variable to have a mean of 0 and sd of 1<\/span><\/strong>\n<strong>df &lt;- scale(df)\n\n<span style=\"color: #008080;\">#view first six rows of dataset<\/span>\nhead(df)\n\n               Murder Assault UrbanPop Rape\nAlabama 1.24256408 0.7828393 -0.5209066 -0.003416473\nAlaska 0.50786248 1.1068225 -1.2117642 2.484202941\nArizona 0.07163341 1.4788032 0.9989801 1.042878388\nArkansas 0.23234938 0.2308680 -1.0735927 -0.184916602\nCalifornia 0.27826823 1.2628144 1.7589234 2.067820292\nColorado 0.02571456 0.3988593 0.8608085 1.864967207\n<\/strong><\/pre>\n<h3><span style=\"color: #000000;\"><strong>\u7b2c 3 \u6b65\uff1a\u627e\u5230\u6700\u4f73\u7c07\u6570<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u8981\u5728 R \u4e2d\u6267\u884c k-means \u805a\u7c7b\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528\u5185\u7f6e\u7684<strong>kmeans()<\/strong>\u51fd\u6570\uff0c\u8be5\u51fd\u6570\u4f7f\u7528\u4ee5\u4e0b\u8bed\u6cd5\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>kmeans\uff08\u6570\u636e\u3001\u4e2d\u5fc3\u3001nstart\uff09<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u91d1\u5b50\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\"><strong>\u6570\u636e\uff1a<\/strong>\u6570\u636e\u96c6\u7684\u540d\u79f0\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>\u4e2d\u5fc3\uff1a<\/strong>\u7c07\u7684\u6570\u91cf\uff0c\u8868\u793a\u4e3a<em>k<\/em> \u3002<\/span><\/li>\n<li> <span style=\"color: #000000;\"><strong>nstart\uff1a<\/strong>\u521d\u59cb\u914d\u7f6e\u7684\u6570\u91cf\u3002\u7531\u4e8e\u4e0d\u540c\u7684\u521d\u59cb\u542f\u52a8\u7c07\u53ef\u80fd\u4f1a\u5bfc\u81f4\u4e0d\u540c\u7684\u7ed3\u679c\uff0c\u56e0\u6b64\u5efa\u8bae\u4f7f\u7528\u51e0\u79cd\u4e0d\u540c\u7684\u521d\u59cb\u914d\u7f6e\u3002 k-means \u7b97\u6cd5\u5c06\u627e\u5230\u5bfc\u81f4\u7c07\u5185\u53d8\u5316\u6700\u5c0f\u7684\u521d\u59cb\u914d\u7f6e\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7531\u4e8e\u6211\u4eec\u4e8b\u5148\u4e0d\u77e5\u9053\u6709\u591a\u5c11\u4e2a\u96c6\u7fa4\u662f\u6700\u4f73\u7684\uff0c\u56e0\u6b64\u6211\u4eec\u5c06\u521b\u5efa\u4e24\u4e2a\u4e0d\u540c\u7684\u56fe\u8868\u6765\u5e2e\u52a9\u6211\u4eec\u505a\u51fa\u51b3\u5b9a\uff1a<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>1. \u7c07\u6570\u76f8\u5bf9\u4e8e\u603b\u6570\u7684\u5e73\u65b9\u548c<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u9996\u5148\uff0c\u6211\u4eec\u5c06\u4f7f\u7528<strong>fviz_nbclust()<\/strong>\u51fd\u6570\u521b\u5efa\u7c07\u6570\u4e0e\u5e73\u65b9\u548c\u603b\u6570\u7684\u5173\u7cfb\u56fe\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong>fviz_nbclust(df, kmeans, method = \u201c <span style=\"color: #008000;\">wss<\/span> \u201d)<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12310 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/kmmoyenne1.png\" alt=\"k-means \u805a\u7c7b\u4e2d\u7684\u6700\u4f73\u805a\u7c7b\u6570\" width=\"444\" height=\"434\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u901a\u5e38\uff0c\u5f53\u6211\u4eec\u521b\u5efa\u6b64\u7c7b\u56fe\u65f6\uff0c\u6211\u4eec\u4f1a\u5bfb\u627e\u5e73\u65b9\u548c\u5f00\u59cb\u201c\u5f2f\u66f2\u201d\u6216\u8d8b\u4e8e\u5e73\u5766\u7684\u201c\u62d0\u70b9\u201d\u3002\u8fd9\u901a\u5e38\u662f\u6700\u4f73\u7684\u7c07\u6570\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u5bf9\u4e8e\u8be5\u56fe\uff0c\u5728 k = 4 \u4e2a\u7c07\u5904\u4f3c\u4e4e\u5b58\u5728\u5c0f\u626d\u7ed3\u6216\u201c\u5f2f\u66f2\u201d\u3002<\/span><\/p>\n<p> <span style=\"color: #000000;\"><strong>2. \u805a\u7c7b\u6570\u91cf\u4e0e\u95f4\u9699\u7edf\u8ba1<\/strong><\/span><\/p>\n<p><span style=\"color: #000000;\">\u786e\u5b9a\u6700\u4f73\u7c07\u6570\u7684\u53e6\u4e00\u79cd\u65b9\u6cd5\u662f\u4f7f\u7528\u79f0\u4e3a<a style=\"color: #000000;\" href=\"https:\/\/web.stanford.edu\/~hastie\/Papers\/gap.pdf\" target=\"_blank\" rel=\"noopener noreferrer\">\u504f\u5dee\u7edf\u8ba1\u91cf<\/a>\u7684\u5ea6\u91cf\uff0c\u5b83\u5c06\u4e0d\u540c k \u503c\u7684\u7c07\u5185\u603b\u53d8\u5f02\u4e0e\u672a\u8fdb\u884c\u805a\u7c7b\u7684\u5206\u5e03\u7684\u9884\u671f\u503c\u8fdb\u884c\u6bd4\u8f83\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<em>cluster<\/em>\u5305\u4e2d\u7684<strong>clusGap()<\/strong>\u51fd\u6570\u8ba1\u7b97\u6bcf\u4e2a\u7c07\u6570\u91cf\u7684\u95f4\u9699\u7edf\u8ba1\u91cf\uff0c\u5e76\u4f7f\u7528<strong>fviz_gap_stat()<\/strong>\u51fd\u6570\u6839\u636e\u95f4\u9699\u7edf\u8ba1\u91cf\u7ed8\u5236\u7c07\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#calculate gap statistic based on number of clusters\n<\/span>gap_stat &lt;- clusGap(df,\n                    FUN = kmeans,\n                    nstart = 25,\n                    K.max = 10,\n                    B = 50)\n\n<span style=\"color: #008080;\">#plot number of clusters vs. gap statistic\n<\/span>fviz_gap_stat(gap_stat)<\/strong> <\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12311 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/moyenne-km2.png\" alt=\"\u6700\u4f73\u7c07\u6570\u7684\u504f\u5dee\u7edf\u8ba1\" width=\"454\" height=\"445\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u4ece\u56fe\u4e2d\u6211\u4eec\u53ef\u4ee5\u770b\u5230\uff0c\u95f4\u9699\u7edf\u8ba1\u91cf\u5728 k = 4 \u4e2a\u7c07\u65f6\u6700\u9ad8\uff0c\u8fd9\u5bf9\u5e94\u4e8e\u6211\u4eec\u4e4b\u524d\u4f7f\u7528\u7684\u8098\u6cd5\u3002<\/span><\/p>\n<h3><span style=\"color: #000000;\"><strong>\u6b65\u9aa4 4\uff1a\u4f7f\u7528\u6700\u4f73<em>K<\/em>\u6267\u884c K \u5747\u503c\u805a\u7c7b<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">\u6700\u540e\uff0c\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<em>k<\/em>\u7684\u6700\u4f73\u503c 4 \u5bf9\u6570\u636e\u96c6\u6267\u884c k \u5747\u503c\u805a\u7c7b\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#make this example reproducible\n<span style=\"color: #000000;\">set.seed(1)<\/span>\n\n#perform k-means clustering with k = 4 clusters\n<\/span>km &lt;- kmeans(df, centers = 4, nstart = 25)\n\n<span style=\"color: #008080;\">#view results\n<\/span>km\n\nK-means clustering with 4 clusters of sizes 16, 13, 13, 8\n\nCluster means:\n      Murder Assault UrbanPop Rape\n1 -0.4894375 -0.3826001 0.5758298 -0.26165379\n2 -0.9615407 -1.1066010 -0.9301069 -0.96676331\n3 0.6950701 1.0394414 0.7226370 1.27693964\n4 1.4118898 0.8743346 -0.8145211 0.01927104\n\nVector clustering:\n       Alabama Alaska Arizona Arkansas California Colorado \n             4 3 3 4 3 3 \n   Connecticut Delaware Florida Georgia Hawaii Idaho \n             1 1 3 4 1 2 \n      Illinois Indiana Iowa Kansas Kentucky Louisiana \n             3 1 2 1 2 4 \n         Maine Maryland Massachusetts Michigan Minnesota Mississippi \n             2 3 1 3 2 4 \n      Missouri Montana Nebraska Nevada New Hampshire New Jersey \n             3 2 2 3 2 1 \n    New Mexico New York North Carolina North Dakota Ohio Oklahoma \n             3 3 4 2 1 1 \n        Oregon Pennsylvania Rhode Island South Carolina South Dakota Tennessee \n             1 1 1 4 2 4 \n         Texas Utah Vermont Virginia Washington West Virginia \n             3 1 2 1 1 2 \n     Wisconsin Wyoming \n             2 1 \n\nWithin cluster sum of squares by cluster:\n[1] 16.212213 11.952463 19.922437 8.316061\n (between_SS \/ total_SS = 71.2%)\n\nAvailable components:\n\n[1] \"cluster\" \"centers\" \"totss\" \"withinss\" \"tot.withinss\" \"betweenss\"   \n[7] \"size\" \"iter\" \"ifault\"         \n<\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u4ece\u7ed3\u679c\u6211\u4eec\u53ef\u4ee5\u770b\u51fa\uff1a<\/span><\/p>\n<ul>\n<li> <span style=\"color: #000000;\"><b>16 \u4e2a<\/b>\u5dde\u88ab\u5206\u914d\u5230\u7b2c\u4e00\u4e2a\u96c6\u7fa4<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>13\u4e2a<\/strong>\u5dde\u5df2\u88ab\u5206\u914d\u5230\u7b2c\u4e8c\u4e2a\u96c6\u7fa4<\/span><\/li>\n<li><span style=\"color: #000000;\"><strong>13\u4e2a<\/strong>\u5dde\u5df2\u88ab\u5206\u914d\u5230\u7b2c\u4e09\u96c6\u7fa4<\/span><\/li>\n<li><span style=\"color: #000000;\"><b>8\u4e2a<\/b>\u5dde\u5df2\u88ab\u5206\u914d\u5230\u7b2c\u56db\u96c6\u7fa4<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u53ef\u4ee5\u4f7f\u7528<strong>fivz_cluster()<\/strong>\u51fd\u6570\u5728\u6563\u70b9\u56fe\u4e0a\u53ef\u89c6\u5316\u7c07\uff0c\u8be5\u6563\u70b9\u56fe\u5728\u8f74\u4e0a\u663e\u793a\u524d\u4e24\u4e2a\u4e3b\u6210\u5206\uff1a<\/span> <\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#plot results of final k-means model\n<span style=\"color: #000000;\">fviz_cluster(km, data = df)\n<\/span><\/span><\/strong><\/pre>\n<p><img decoding=\"async\" loading=\"lazy\" class=\"aligncenter wp-image-12313 \" src=\"https:\/\/statorials.org\/wp-content\/uploads\/2023\/08\/kmmoyenne4.png\" alt=\"R \u4e2d\u7684 K \u5747\u503c\u805a\u7c7b\u56fe\" width=\"475\" height=\"472\" srcset=\"\" sizes=\"auto, \"><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u4f7f\u7528<strong>Aggregate()<\/strong>\u51fd\u6570\u6765\u67e5\u627e\u6bcf\u4e2a\u7c07\u4e2d\u53d8\u91cf\u7684\u5e73\u5747\u503c\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#find means of each cluster\n<span style=\"color: #000000;\">aggregate(USArrests, by= <span style=\"color: #3366ff;\">list<\/span> (cluster=km$cluster), mean)\n\ncluster Murder Assault UrbanPop Rape\n\t\t\t\t\n1 3.60000 78.53846 52.07692 12.17692\n2 10.81538 257.38462 76.00000 33.19231\n3 5.65625 138.87500 73.87500 18.78125\n4 13.93750 243.62500 53.75000 21.41250\n<\/span><\/span><\/strong><\/pre>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u5c06\u6b64\u8f93\u51fa\u89e3\u91ca\u5982\u4e0b\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u7b2c\u4e00\u7ec4\u5dde\u4e2d\u6bcf 10 \u4e07\u516c\u6c11\u7684\u5e73\u5747\u8c0b\u6740\u6848\u6570\u91cf\u4e3a<strong>3.6 \u8d77<\/strong>\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7b2c\u4e00\u7ec4\u56fd\u5bb6\u4e2d\u6bcf 10 \u4e07\u516c\u6c11\u7684\u5e73\u5747\u88ad\u51fb\u6b21\u6570\u4e3a<strong>78.5 \u6b21<\/strong>\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7b2c\u4e00\u7ec4\u5dde\u4e2d\u5c45\u4f4f\u5728\u57ce\u5e02\u5730\u533a\u7684\u5c45\u6c11\u7684\u5e73\u5747\u6bd4\u4f8b\u4e3a<b>52.1%<\/b> \u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u7b2c 1 \u7ec4\u5dde\u6bcf 10 \u4e07\u516c\u6c11\u5e73\u5747\u53d1\u751f\u5f3a\u5978\u6848<strong>12.2<\/strong>\u8d77<strong>\u3002<\/strong><\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7b49\u7b49\u3002<\/span><\/p>\n<p><span style=\"color: #000000;\">\u6211\u4eec\u8fd8\u53ef\u4ee5\u5c06\u6bcf\u4e2a\u72b6\u6001\u7684\u805a\u7c7b\u5206\u914d\u6dfb\u52a0\u5230\u539f\u59cb\u6570\u636e\u96c6\u4e2d\uff1a<\/span><\/p>\n<pre style=\"background-color: #ececec; font-size: 15px;\"> <strong><span style=\"color: #008080;\">#add cluster assignment to original data\n<span style=\"color: #000000;\">final_data &lt;- cbind(USArrests, cluster = km$cluster)\n<\/span>\n#view final data\n<span style=\"color: #000000;\">head(final_data)\n\n\t<\/span><span style=\"color: #000000;\">Murder Assault UrbanPop<\/span> <span style=\"color: #000000;\">Rape<\/span> <span style=\"color: #000000;\">cluster\n\t\t\t\t\nAlabama<\/span> <span style=\"color: #000000;\">13.2<\/span> <span style=\"color: #000000;\">236 58<\/span> <span style=\"color: #000000;\">21.2<\/span> <span style=\"color: #000000;\">4\nAlaska<\/span> <span style=\"color: #000000;\">10.0 263 48<\/span> <span style=\"color: #000000;\">44.5<\/span> <span style=\"color: #000000;\">2\nArizona<\/span> <span style=\"color: #000000;\">8.1 294 80<\/span> <span style=\"color: #000000;\">31.0<\/span> <span style=\"color: #000000;\">2\nArkansas<\/span> <span style=\"color: #000000;\">8.8 190 50<\/span> <span style=\"color: #000000;\">19.5<\/span> <span style=\"color: #000000;\">4\nCalifornia<\/span> <span style=\"color: #000000;\">9.0 276 91<\/span> <span style=\"color: #000000;\">40.6<\/span> <span style=\"color: #000000;\">2\nColorado<\/span> <span style=\"color: #000000;\">7.9 204 78<\/span> <span style=\"color: #000000;\">38.7<\/span> <span style=\"color: #000000;\">2\n<\/span><\/span><\/strong><\/pre>\n<h3> <span style=\"color: #000000;\"><strong>K-Means \u805a\u7c7b\u7684\u4f18\u70b9\u548c\u7f3a\u70b9<\/strong><\/span><\/h3>\n<p><span style=\"color: #000000;\">K-means \u805a\u7c7b\u5177\u6709\u4ee5\u4e0b\u4f18\u70b9\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u8fd9\u662f\u4e00\u79cd\u5feb\u901f\u7b97\u6cd5\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5b83\u53ef\u4ee5\u5f88\u597d\u5730\u5904\u7406\u5927\u578b\u6570\u636e\u96c6\u3002<\/span><\/li>\n<\/ul>\n<p><span style=\"color: #000000;\">\u7136\u800c\uff0c\u5b83\u6709\u4ee5\u4e0b\u6f5c\u5728\u7684\u7f3a\u70b9\uff1a<\/span><\/p>\n<ul>\n<li><span style=\"color: #000000;\">\u8fd9\u9700\u8981\u6211\u4eec\u5728\u8fd0\u884c\u7b97\u6cd5\u4e4b\u524d\u6307\u5b9a\u7c07\u7684\u6570\u91cf\u3002<\/span><\/li>\n<li><span style=\"color: #000000;\">\u5b83\u5bf9\u5f02\u5e38\u503c\u5f88\u654f\u611f\u3002<\/span><\/li>\n<\/ul>\n<p> <span style=\"color: #000000;\">k \u5747\u503c\u805a\u7c7b\u7684\u4e24\u79cd\u66ff\u4ee3\u65b9\u6cd5\u662f<a href=\"https:\/\/statorials.org\/cn\/r-\u4e2d\u7684-k-\u4e2a\u4e2d\u5fc3\u70b9\/\" target=\"_blank\" rel=\"noopener noreferrer\">k \u5747\u503c\u805a\u7c7b<\/a>\u548c\u5c42\u6b21\u805a\u7c7b\u3002<\/span><\/p>\n<hr>\n<p><span style=\"color: #000000;\">\u60a8\u53ef\u4ee5<a href=\"https:\/\/github.com\/Statorials\/R-Guides\/blob\/main\/k_means.R\" target=\"_blank\" rel=\"noopener noreferrer\">\u5728\u6b64\u5904<\/a>\u627e\u5230\u672c\u793a\u4f8b\u4e2d\u4f7f\u7528\u7684\u5b8c\u6574 R \u4ee3\u7801\u3002<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u805a\u7c7b\u662f\u4e00\u79cd\u673a\u5668\u5b66\u4e60\u6280\u672f\uff0c\u5c1d\u8bd5\u5728\u6570\u636e\u96c6\u4e2d\u67e5\u627e\u89c2\u5bdf\u7ec4\u3002 \u76ee\u6807\u662f\u627e\u5230\u805a\u7c7b\uff0c\u4f7f\u5f97\u6bcf\u4e2a\u805a\u7c7b\u5185\u7684\u89c2\u5bdf\u7ed3\u679c\u5f7c\u6b64\u975e\u5e38\u76f8\u4f3c\uff0c\u800c\u4e0d [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[11],"tags":[],"class_list":["post-1245","post","type-post","status-publish","format-standard","hentry","category-11"],"yoast_head":"<!-- This site is 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