{"id":3619,"date":"2023-07-16T12:08:13","date_gmt":"2023-07-16T12:08:13","guid":{"rendered":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a1%e1%86%af%e1%84%81%e1%85%ae%e1%86%b7%e1%84%8e%e1%85%b5-%e1%84%87%e1%85%a1%e1%86%bc%e1%84%87%e1%85%a5%e1%86%b8\/"},"modified":"2023-07-16T12:08:13","modified_gmt":"2023-07-16T12:08:13","slug":"r%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a1%e1%86%af%e1%84%81%e1%85%ae%e1%86%b7%e1%84%8e%e1%85%b5-%e1%84%87%e1%85%a1%e1%86%bc%e1%84%87%e1%85%a5%e1%86%b8","status":"publish","type":"post","link":"https:\/\/statorials.org\/ko\/r%e1%84%8b%e1%85%b4-%e1%84%91%e1%85%a1%e1%86%af%e1%84%81%e1%85%ae%e1%86%b7%e1%84%8e%e1%85%b5-%e1%84%87%e1%85%a1%e1%86%bc%e1%84%87%e1%85%a5%e1%86%b8\/","title":{"rendered":"R\uc5d0\uc11c elbow \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\uc5ec \ucd5c\uc801\uc758 \ud074\ub7ec\uc2a4\ud130\ub97c \ucc3e\ub294 \ubc29\ubc95"},"content":{"rendered":"

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\uae30\uacc4 \ud559\uc2b5<\/a> \uc5d0\uc11c \uac00\uc7a5 \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998 \uc911 \ud558\ub098\ub294 k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1<\/strong> \uc73c\ub85c \uc54c\ub824\uc838 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n

K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \ub370\uc774\ud130 \uc138\ud2b8\uc758 \uac01 \uad00\uce21\uce58\ub97c K<\/em> \ud074\ub7ec\uc2a4\ud130 \uc911 \ud558\ub098\uc5d0 \ubc30\uce58\ud558\ub294 \uae30\uc220\uc785\ub2c8\ub2e4.<\/span><\/p>\n

\ucd5c\uc885 \ubaa9\ud45c\ub294 \uac01 \ud074\ub7ec\uc2a4\ud130 \ub0b4\uc758 \uad00\uce21\uce58\uac00 \uc11c\ub85c \ub9e4\uc6b0 \uc720\uc0ac\ud55c \ubc18\uba74 \ub2e4\ub978 \ud074\ub7ec\uc2a4\ud130\uc758 \uad00\uce21\uce58\ub294 \uc11c\ub85c \uc0c1\ub2f9\ud788 \ub2e4\ub978 K<\/em> \uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\ub97c \uac16\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

k-\ud3c9\uade0 \uad70\uc9d1\ud654\ub97c \uc218\ud589\ud560 \ub54c \uccab \ubc88\uc9f8 \ub2e8\uacc4\ub294 \uad00\uce21\uac12\uc744 \ubc30\uce58\ud558\ub824\ub294 \uad70\uc9d1 \uc218\uc778 K<\/em> \uac12\uc744 \uc120\ud0dd\ud558\ub294 \uac83\uc785\ub2c8\ub2e4.<\/span><\/p>\n

K<\/em> \uac12\uc744 \uc120\ud0dd\ud558\ub294 \uac00\uc7a5 \uc77c\ubc18\uc801\uc778 \ubc29\ubc95 \uc911 \ud558\ub098\ub294 \uc5d8\ubcf4\uc6b0 \ubc29\ubc95(elbow method)<\/strong> \uc73c\ub85c, x\ucd95\uc5d0 \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c, y\ucd95\uc5d0 \uc81c\uacf1\ud569\uc758 \ud569\uacc4\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud50c\ub86f\uc744 \ub9cc\ub4e0 \ub2e4\uc74c \ub2e4\uc74c\uc744 \uc2dd\ubcc4\ud569\ub2c8\ub2e4. \ud50c\ub86f\uc5d0 “\ubb34\ub98e” \ub610\ub294 \ud68c\uc804\uc774 \ub098\ud0c0\ub098\ub294 \uacf3\uc785\ub2c8\ub2e4.<\/span><\/p>\n

“\ubb34\ub98e”\uc774 \ubc1c\uc0dd\ud558\ub294 x\ucd95 \uc9c0\uc810\uc740 k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998\uc5d0 \uc0ac\uc6a9\ud560 \ucd5c\uc801\uc758 \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

\ub2e4\uc74c \uc608\uc81c\uc5d0\uc11c\ub294 R\uc5d0\uc11c \ud314\uafc8\uce58 \ubc29\ubc95\uc744 \uc0ac\uc6a9\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

\uc608: R\uc5d0\uc11c \ud314\uafc8\uce58 \ubc29\ubc95 \uc0ac\uc6a9<\/strong><\/span><\/h2>\n

\uc774 \uc608\uc5d0\uc11c\ub294 R\uc5d0 \ub0b4\uc7a5\ub41c USArrests<\/strong> \ub370\uc774\ud130\uc138\ud2b8\ub97c \uc0ac\uc6a9\ud569\ub2c8\ub2e4. \uc774 \ub370\uc774\ud130\uc138\ud2b8\uc5d0\ub294 1973\ub144 \ubbf8\uad6d \uac01 \uc8fc\uc5d0\uc11c 100,000\uba85\ub2f9 \uc0b4\uc778, \ud3ed\ud589, \uac15\uac04\uc73c\ub85c \uccb4\ud3ec\ub41c \uac74\uc218\uc640 \uac01 \uc8fc\uc758 \ub3c4\uc2dc \uc778\uad6c \ube44\uc728\uc774 \ud3ec\ud568\ub418\uc5b4 \uc788\uc2b5\ub2c8\ub2e4. \uc9c0\uc5ed. , \uc5b4\ubc18\ud31d.<\/span><\/p>\n

\ub2e4\uc74c \ucf54\ub4dc\ub294 \ub370\uc774\ud130\uc138\ud2b8\ub97c \ub85c\ub4dc\ud558\uace0, \ub204\ub77d\ub41c \uac12\uc774 \uc788\ub294 \ud589\uc744 \uc81c\uac70\ud558\uace0, \ud3c9\uade0\uc774 0\uc774\uace0 \ud45c\uc900\ud3b8\ucc28\uac00 1\uc774 \ub418\ub3c4\ub85d \ub370\uc774\ud130\uc138\ud2b8\uc758 \uac01 \ubcc0\uc218\ub97c \uc870\uc815\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4.<\/span><\/p>\n

 #load data<\/span>\ndf <-USArrests\n\n#remove rows with missing values<\/span><\/strong>\ndf <- na. omitted<\/span> (df)\n\n#scale each variable to have a mean of 0 and sd of 1<\/span><\/strong>\ndf <- scale(df)\n\n#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
 k-\ud3c9\uade0 \uc54c\uace0\ub9ac\uc998\uc5d0 \uc0ac\uc6a9\ud560 \ucd5c\uc801\uc758 \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \ucc3e\uae30 \uc704\ud574 Factoextra<\/strong> \ud328\ud0a4\uc9c0\uc758 fviz_nbclust()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud074\ub7ec\uc2a4\ud130 \uc218 \ub300 \uc81c\uacf1\ud569\uc758 \ucd1d\uacc4\uc5d0 \ub300\ud55c \ud50c\ub86f\uc744 \ub9cc\ub4ed\ub2c8\ub2e4.<\/span> <\/p>\n
 library<\/span> (cluster)\nlibrary<\/span> (factoextra)\n\n#create plot of number of clusters vs total within sum of squares<\/span>\nfviz_nbclust(df, kmeans, method = \u201c wss<\/span> \u201d)<\/strong> <\/pre>\n
\"R-\ubca4\ub4dc<\/p>\n
 \uc774 \uadf8\ub798\ud504\uc5d0\uc11c\ub294 k = 4\uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\uc5d0 “\ubb34\ub98e” \ub610\ub294 \uaf2c\uc784\uc774 \uc788\ub294 \uac83\uc73c\ub85c \ub098\ud0c0\ub0a9\ub2c8\ub2e4. \uc774 \uc9c0\uc810\uc740 \uc81c\uacf1\ud569\uc758 \ud569\uacc4\uac00 \uc548\uc815\ud654\ub418\uae30 \uc2dc\uc791\ud558\ub294 \uc9c0\uc810\uc785\ub2c8\ub2e4.<\/span><\/p>\n
 \uc774\ub294 k-\ud3c9\uade0 \uc54c\uace0\ub9ac\uc998\uc5d0 \uc0ac\uc6a9\ud560 \ucd5c\uc801\uc758 \ud074\ub7ec\uc2a4\ud130 \uc218\uac00 4\uc784\uc744 \uc54c\ub824\uc90d\ub2c8\ub2e4.<\/span><\/p>\n
 \ud074\ub7ec\uc2a4\ud130<\/strong> \ud328\ud0a4\uc9c0\uc758 kmeans()<\/strong> \ud568\uc218\ub97c \uc0ac\uc6a9\ud558\uba74 k<\/em> \/4\uc5d0 \ub300\ud55c \ucd5c\uc801 \uac12\uc744 \uc0ac\uc6a9\ud558\uc5ec \ub370\uc774\ud130\uc138\ud2b8\uc5d0\uc11c k-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 #make this example reproducible\nset. seeds<\/span> (1)<\/span>\n\n#perform k-means clustering with k = 4 clusters\n<\/span>km <- kmeans(df, centers = 4, nstart = 25)\n\n#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
 \uacb0\uacfc\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ub2e4\uc74c\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n
 \ucc38\uace0<\/strong> : \ub354 \ub9ce\uc740 \ud074\ub7ec\uc2a4\ud130\ub97c \uc0ac\uc6a9\ud558\uba74 \uc81c\uacf1\ud569\uc5d0\uc11c \ub354 \ub0ae\uc740 \ucd1d\uacc4\ub97c \uc5bb\uc744 \uc218 \uc788\uc9c0\ub9cc \ud6c8\ub828 \ub370\uc774\ud130\uc5d0 \uacfc\uc801\ud569\ub420 \uac00\ub2a5\uc131\uc774 \ub192<\/a> \uc73c\ubbc0\ub85c k-\ud3c9\uade0 \uc54c\uace0\ub9ac\uc998\uc774 \ud14c\uc2a4\ud2b8 \ub370\uc774\ud130\uc5d0\uc11c \uc798 \uc791\ub3d9\ud558\uc9c0 \uc54a\uc2b5\ub2c8\ub2e4.<\/span><\/p>\n