\ub370\uc774\ud130 \ud3ec\uc778\ud2b8 \ud560\ub2f9 \ubc0f \uc911\uc2ec\uc810 \uc7ac\uacc4\uc0b0 \uacfc\uc815\uc744 \ubc18\ubcf5\ud569\ub2c8\ub2e4.<\/li>\n<\/ol>\n2.1 K-\ud3c9\uade0 \uc54c\uace0\ub9ac\uc998\uc758 \uc218\ud559\uc801 \ubc30\uacbd<\/h3>\n
K-\ud3c9\uade0 \uad70\uc9d1\ud654\uc758 \ud575\uc2ec\uc740 \uad70\uc9d1 \uac04\uc758 \uac70\ub9ac\ub97c \ucd5c\uc18c\ud654\ud558\ub294 \uac83\uc785\ub2c8\ub2e4. \uc774\ub97c \uc704\ud574 \uac01 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc640 \uad70\uc9d1 \uc911\uc2ec \uc0ac\uc774\uc758 \uc720\ud074\ub9ac\ub4dc \uac70\ub9ac(Euclidean distance)\ub97c \uacc4\uc0b0\ud558\uace0, \uad70\uc9d1 \uc911\uc2ec\uc744 \uc7ac\uacc4\uc0b0\ud569\ub2c8\ub2e4. \ucd5c\uc885\uc801\uc73c\ub85c \uac01 \uad70\uc9d1\uc758 \uc911\uc2ec\uc740 \ud574\ub2f9 \uad70\uc9d1\uc5d0 \uc18d\ud55c \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \ud3c9\uade0\uac12\uc73c\ub85c \uacb0\uc815\ub429\ub2c8\ub2e4.<\/p>\n
3. K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub97c \ud2b8\ub808\uc774\ub529\uc5d0 \uc801\uc6a9\ud558\uae30<\/h2>\n
K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub294 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc5d0 \uc5ec\ub7ec \ubc29\uc2dd\uc73c\ub85c \ud65c\uc6a9\ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc8fc\ub85c \uc2dc\uc7a5 \ub370\uc774\ud130\ub97c \ubd84\uc11d\ud558\uc5ec \ud2b9\uc131\uc5d0 \ub530\ub77c \uadf8\ub8f9\ud654\ud558\uac70\ub098, \ud2b9\uc815 \uc790\uc0b0\uc758 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uad6c\uc131\uc5d0 \ud65c\uc6a9\ub429\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uacfc\uac70 \uc8fc\uac00 \ub370\uc774\ud130\ub97c \uad70\uc9d1\ud654\ud558\uc5ec \uc720\uc0ac\ud55c \ud589\ub3d9 \ud328\ud134\uc744 \ubcf4\uc774\ub294 \uc790\uc0b0\uc744 \uadf8\ub8f9\uc73c\ub85c \ubb36\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.1 \uc790\uc0b0 \uad70\uc9d1\ud654<\/h3>\n
\uc8fc\uc2dd \uc2dc\uc7a5\uc5d0\uc11c \ub2e4\uc591\ud55c \uc8fc\uc2dd\ub4e4\uc740 \uc11c\ub85c \uc0c1\uad00\uad00\uacc4\uac00 \uc788\uc2b5\ub2c8\ub2e4. K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub97c \ud1b5\ud574 \uc720\uc0ac\ud55c \ud589\ub3d9\uc744 \ubcf4\uc774\ub294 \uc8fc\uc2dd\ub4e4\uc744 \uc2dd\ubcc4\ud568\uc73c\ub85c\uc368 \ud3ec\ud2b8\ud3f4\ub9ac\uc624\ub97c \ucd5c\uc801\ud654\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc608\ub97c \ub4e4\uc5b4, \uae30\uc220 \uc0b0\uc5c5 \ub0b4\uc758 \uc8fc\uc2dd\ub4e4\uc744 \uad70\uc9d1\ud654\ud558\uc5ec \ud2b9\uc815 \uad70\uc9d1\uc758 \uc8fc\uc2dd\uc5d0 \uc9d1\uc911 \ud22c\uc790\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
3.2 \uac70\ub798 \uc2dc\uc810 \uacb0\uc815<\/h3>\n
K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub97c \ud1b5\ud574 \uad70\uc9d1\uc758 \ud328\ud134\uc744 \ubd84\uc11d\ud568\uc73c\ub85c\uc368 \ud2b9\uc815 \uad70\uc9d1\uc758 \ud3c9\uade0\uc801 \ud589\ub3d9\uc744 \uc774\ud574\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc774\ub97c \ubc14\ud0d5\uc73c\ub85c \uac01 \uad70\uc9d1\uc758 \uc9c4\uc785 \uc2dc\uc810 \ubc0f \uc774\ud0c8 \uc2dc\uc810\uc744 \uacb0\uc815\ud560 \uc218 \uc788\uc73c\uba70, \uc774\ub97c \ud1b5\ud574 \ub192\uc740 \uc218\uc775\ub960\uc744 \uc5bb\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4. K-\ud3c9\uade0 \uad70\uc9d1\ud654\uc758 \uad6c\ud604<\/h2>\n
K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub97c \uad6c\ud604\ud558\uae30 \uc704\ud574\uc11c\ub294 \ud30c\uc774\uc36c\uc758 scikit-learn<\/code> \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc0ac\uc6a9\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ub2e4\uc74c \uc608\uc81c\ub294 K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub97c \ud1b5\ud574 \uc8fc\uc2dd \ub370\uc774\ud130\ub97c \uad70\uc9d1\ud654\ud558\ub294 \ubc29\ubc95\uc744 \ubcf4\uc5ec\uc90d\ub2c8\ub2e4:<\/p>\n\nimport pandas as pd\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\n\n# \ub370\uc774\ud130 \ub85c\ub4dc\ndata = pd.read_csv('stock_data.csv') # \uac00\uc0c1\uc758 \uc8fc\uc2dd \ub370\uc774\ud130\n\n# \ud544\uc694\ud55c \ud53c\uccd0 \uc120\ud0dd\nfeatures = data[['feature1', 'feature2']] # \uc608: \uac00\uaca9, \uac70\ub798\ub7c9\n\n# K-\ud3c9\uade0 \uad70\uc9d1\ud654 \uc218\ud589\nkmeans = KMeans(n_clusters=3)\ndata['cluster'] = kmeans.fit_predict(features)\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.scatter(data['feature1'], data['feature2'], c=data['cluster'])\nplt.xlabel('Feature 1')\nplt.ylabel('Feature 2')\nplt.title('K-Means Clustering Results')\nplt.show()\n<\/code><\/pre>\n4.1 \uad70\uc9d1\uc758 \uc218 \uacb0\uc815\ud558\uae30<\/h3>\n
K-\ud3c9\uade0 \uad70\uc9d1\ud654\uc5d0\uc11c \uad70\uc9d1\uc758 \uc218 K\ub97c \uacb0\uc815\ud558\ub294 \uac83\uc740 \uc911\uc694\ud55c \ubb38\uc81c\uc785\ub2c8\ub2e4. \uc5d8\ubcf4 \ubc29\ubc95(Elbow Method)\uc740 \uad70\uc9d1 \uc218\ub97c \uacb0\uc815\ud560 \ub54c \uc720\uc6a9\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. \uad70\uc9d1 \uc218\ub97c \uc99d\uac00\uc2dc\ud0a4\uba74\uc11c \ucd1d \uc81c\uacf1 \uc624\ucc28(SSE)\uac00 \uc5bc\ub9c8\ub098 \uc904\uc5b4\ub4dc\ub294\uc9c0\ub97c \uad00\ucc30\ud558\uc5ec, \ubcc0\ud654\uac00 \uc644\ub9cc\ud574\uc9c0\ub294 \uc9c0\uc810\uc744 \ucc3e\uc2b5\ub2c8\ub2e4.<\/p>\n
\nsse = []\nK_range = range(1, 11)\n\nfor k in K_range:\n kmeans = KMeans(n_clusters=k)\n kmeans.fit(features)\n sse.append(kmeans.inertia_)\n\nplt.plot(K_range, sse)\nplt.xlabel('Number of clusters K')\nplt.ylabel('SSE')\nplt.title('Elbow Method')\nplt.show()\n<\/code><\/pre>\n5. K-\ud3c9\uade0 \uad70\uc9d1\ud654\uc758 \ud55c\uacc4<\/h2>\n
K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub294 \uc720\uc6a9\ud55c \uae30\ubc95\uc774\uc9c0\ub9cc \uba87 \uac00\uc9c0 \ud55c\uacc4\uac00 \uc874\uc7ac\ud569\ub2c8\ub2e4. \ub2e4\ub978 \uad70\uc9d1\ud654 \uae30\ubc95\uc5d0 \ube44\ud574 \ub2e4\uc74c\uacfc \uac19\uc740 \ubb38\uc81c\uc810\uc774 \uc788\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uad70\uc9d1 \uc218 K\ub97c \uc0ac\uc804\uc5d0 \uc9c0\uc815\ud574\uc57c \ud569\ub2c8\ub2e4.<\/li>\n
- \ucd08\uae30 \uc911\uc2ec\uc810\uc758 \uc120\ud0dd\uc5d0 \ub530\ub77c \ucd5c\uc885 \uacb0\uacfc\uac00 \ub2ec\ub77c\uc9c8 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n
- \ube44\uc2b7\ud55c \ud06c\uae30\uc640 \ubc00\ub3c4\uc758 \uad70\uc9d1\uc744 \uac00\uc815\ud558\ubbc0\ub85c \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \uad6c\uc870\ub97c \uc798 \ud45c\ud604\ud558\uc9c0 \ubabb\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/li>\n<\/ul>\n
6. \uacb0\ub860<\/h2>\n
K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub294 \uba38\uc2e0\ub7ec\ub2dd\uc744 \ud65c\uc6a9\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc5d0\uc11c \uc911\uc694\ud55c \ub3c4\uad6c\uac00 \ub420 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \uc790\uc0b0\uc758 \ud328\ud134\uc744 \uc774\ud574\ud558\uace0, \ud6a8\uc728\uc801\uc778 \ud3ec\ud2b8\ud3f4\ub9ac\uc624 \uad6c\uc131 \ubc0f \uac70\ub798 \uc2dc\uc810\uc744 \uacb0\uc815\ud558\ub294 \ub370 \uc720\uc6a9\ud569\ub2c8\ub2e4. \uc774 \uac15\uc88c\uc5d0\uc11c\ub294 K-\ud3c9\uade0 \uad70\uc9d1\ud654\uc758 \uc774\ub860\uc801 \ubc30\uacbd\uacfc \uc2e4\uc9c8\uc801\uc778 \uc801\uc6a9\ubc95\uc5d0 \ub300\ud574 \uc0b4\ud3b4\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4\uc5d0\ub294 K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub97c \uae30\ubc18\uc73c\ub85c \ud55c \ub2e4\uc591\ud55c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \ubc1c\uc804\uc2dc\ud0a4\ub294 \ub370 \ud544\uc694\ud55c \uae30\ucd08\uac00 \ub418\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.<\/p>\n
\uc774\uc81c \uc5ec\ub7ec\ubd84\uc774 K-\ud3c9\uade0 \uad70\uc9d1\ud654\ub97c \ud65c\uc6a9\ud574 \ub354 \ub098\uc740 \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc744 \uac1c\ubc1c\ud558\uc2dc\uae30\ub97c \uae30\ub300\ud569\ub2c8\ub2e4. \ub370\uc774\ud130 \ubd84\uc11d\uc758 \uae4a\uc774\ub97c \ub354\ud558\uace0, \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \ud798\uc744 \uc774\uc6a9\ud558\uc5ec \ud000\ud2b8 \ud2b8\ub808\uc774\ub529\uc758 \uc0c8\ub85c\uc6b4 \uc9c0\ud3c9\uc744 \uc5ec\ub294 \uc5ec\uc815\uc744 \uc2dc\uc791\ud574 \ubcf4\uc138\uc694!<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
\ud000\ud2b8 \ud2b8\ub808\uc774\ub529(Quantitative Trading)\uc740 \ud1b5\uacc4 \ubc0f \uc218\ud559\uc801 \ubaa8\ub378\uc744 \ud65c\uc6a9\ud558\uc5ec \uae08\uc735 \uc2dc\uc7a5\uc5d0\uc11c \uc774\uc724\uc744 \ucd94\uad6c\ud558\ub294 \ud22c\uc790 \uc804\ub7b5\uc785\ub2c8\ub2e4. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc740 \uc774\ub7ec\ud55c \ud000\ud2b8 \ud2b8\ub808\uc774\ub529\uc5d0\uc11c \uc911\uc694\ud55c \uc5ed\ud560\uc744 \ud558\uace0 \uc788\uc73c\uba70, \ub370\uc774\ud130 \ubd84\uc11d, \ud328\ud134 \uc778\uc2dd \ubc0f \uc608\uce21 \ubaa8\ub378\ub9c1\uc5d0 \ud65c\uc6a9\ub418\uace0 \uc788\uc2b5\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95 \uc911 \ud558\ub098\uc778 K-\ud3c9\uade0 \uad70\uc9d1\ud654 \uc54c\uace0\ub9ac\uc998\uc744 \uc911\uc2ec\uc73c\ub85c, \uc774\ub97c \ud2b8\ub808\uc774\ub529 \uc804\ub7b5\uc5d0 \uc5b4\ub5bb\uac8c \uc801\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0 \uc54c\uc544\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. 1. \uba38\uc2e0\ub7ec\ub2dd\uacfc \ub525\ub7ec\ub2dd\uc758 \uae30\ucd08 \uba38\uc2e0\ub7ec\ub2dd\uc740 … \ub354 \ubcf4\uae30 “\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, k-\ud3c9\uade0 \uad70\uc9d1\ud654”<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_jetpack_memberships_contains_paid_content":false,"footnotes":""},"categories":[29],"tags":[],"class_list":["post-28678","post","type-post","status-publish","format-standard","hentry","category-29"],"yoast_head":"\n\uba38\uc2e0\ub7ec\ub2dd \ubc0f \ub525\ub7ec\ub2dd \uc54c\uace0\ub9ac\uc998 \ud2b8\ub808\uc774\ub529, k-\ud3c9\uade0 \uad70\uc9d1\ud654 - 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