{"id":30048,"date":"2024-10-28T03:19:20","date_gmt":"2024-10-28T03:19:20","guid":{"rendered":"http:\/\/atmokpo.com\/w\/?p=30048"},"modified":"2024-11-26T06:50:16","modified_gmt":"2024-11-26T06:50:16","slug":"%eb%94%a5%eb%9f%ac%eb%8b%9d-%ed%8c%8c%ec%9d%b4%ed%86%a0%ec%b9%98-%ea%b0%95%ec%a2%8c-%eb%b0%80%eb%8f%84-%ea%b8%b0%eb%b0%98-%ea%b5%b0%ec%a7%91-%eb%b6%84%ec%84%9d","status":"publish","type":"post","link":"https:\/\/atmokpo.com\/w\/30048\/","title":{"rendered":"\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d"},"content":{"rendered":"

<\/p>\n

1. \uc11c\ub860<\/h2>\n

\n \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc740 \ub370\uc774\ud130 \ub9c8\uc774\ub2dd\uc758 \uc911\uc694\ud55c \uae30\ubc95 \uc911 \ud558\ub098\ub85c, \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \ubc00\ub3c4\ub97c \uae30\ubc18\uc73c\ub85c \ud074\ub7ec\uc2a4\ud130\ub97c \uc2dd\ubcc4\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4.
\n \uc774 \uc54c\uace0\ub9ac\uc998\uc740 \ud2b9\ud788 \ube44\uc120\ud615 \ub370\uc774\ud130 \ud615\ud0dc\ub97c \ub2e4\ub8e8\ub294 \ub370 \uc720\uc6a9\ud558\uba70, \uac01 \ud074\ub7ec\uc2a4\ud130\ub294 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \uace0\ubc00\ub3c4 \uc9c0\uc5ed\uc73c\ub85c \uc815\uc758\ub429\ub2c8\ub2e4.
\n \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.
\n \uc6b0\ub9ac\ub294 \uc8fc\uc694 \uac1c\ub150, \uc54c\uace0\ub9ac\uc998, \uadf8\ub9ac\uace0 \uc2e4\uc81c \uad6c\ud604 \uacfc\uc815\uc744 \ub2e8\uacc4\ubcc4\ub85c \uc9c4\ud589\ud560 \uac83\uc785\ub2c8\ub2e4.\n <\/p>\n

2. \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc758 \uac1c\ub150<\/h2>\n

\n \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc758 \uac00\uc7a5 \ub300\ud45c\uc801\uc778 \uc54c\uace0\ub9ac\uc998\uc778 DBSCAN(Density-Based Spatial Clustering of Applications with Noise)\uc740
\n \ub2e4\uc74c\uacfc \uac19\uc740 \uc6d0\ub9ac\uc5d0 \uae30\ubc18\ud569\ub2c8\ub2e4.
\n – \ubc00\ub3c4: \ud2b9\uc815 \uc601\uc5ed \ub0b4 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \uac1c\uc218.
\n – \u03b5-\uc774\uc6c3: \ud2b9\uc815 \ud3ec\uc778\ud2b8\uc5d0\uc11c \u03b5 \uac70\ub9ac \uc774\ub0b4\uc5d0 \uc788\ub294 \ub2e4\ub978 \ud3ec\uc778\ud2b8\ub4e4.
\n – \ud575\uc2ec \ud3ec\uc778\ud2b8: \u03b5-\uc774\uc6c3\uc758 \uac1c\uc218\uac00 \ucd5c\uc18c \ud3ec\uc778\ud2b8 \uc218(minPts) \uc774\uc0c1\uc778 \ud3ec\uc778\ud2b8.
\n – \uacbd\uacc4 \ud3ec\uc778\ud2b8: \ud575\uc2ec \ud3ec\uc778\ud2b8\uc758 \u03b5-\uc774\uc6c3\uc774\uc9c0\ub9cc, \uc790\uccb4\uc801\uc73c\ub85c\ub294 \ud575\uc2ec \ud3ec\uc778\ud2b8\uac00 \uc544\ub2cc \ud3ec\uc778\ud2b8.
\n – \uc7a1\uc74c \ud3ec\uc778\ud2b8: \uc5b4\ub5a4 \ud575\uc2ec \ud3ec\uc778\ud2b8\uc758 \u03b5-\uc774\uc6c3\uc5d0\ub3c4 \uc18d\ud558\uc9c0 \uc54a\ub294 \ud3ec\uc778\ud2b8.\n <\/p>\n

3. \uc54c\uace0\ub9ac\uc998 \uc124\uba85<\/h2>\n

\n DBSCAN \uc54c\uace0\ub9ac\uc998\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \uac04\ub2e8\ud55c \ub2e8\uacc4\ub85c \uc9c4\ud589\ub429\ub2c8\ub2e4:\n <\/p>\n

    \n
  1. \uc784\uc758\uc758 \ud3ec\uc778\ud2b8\ub97c \uc120\ud0dd\ud569\ub2c8\ub2e4.<\/li>\n
  2. \uc120\ud0dd\ud55c \ud3ec\uc778\ud2b8\uc758 \u03b5-\uc774\uc6c3 \ub0b4 \ud3ec\uc778\ud2b8 \uc218\ub97c \uacc4\uc0b0\ud558\uc5ec, \ud575\uc2ec \ud3ec\uc778\ud2b8\uc778\uc9c0 \ud310\ub2e8\ud569\ub2c8\ub2e4.<\/li>\n
  3. \ud575\uc2ec \ud3ec\uc778\ud2b8\ub77c\uba74, \ud074\ub7ec\uc2a4\ud130\ub97c \ud615\uc131\ud558\uace0 \u03b5-\uc774\uc6c3 \ub0b4 \ub2e4\ub978 \ud3ec\uc778\ud2b8\ub4e4\uc744 \ud074\ub7ec\uc2a4\ud130\uc5d0 \ucd94\uac00\ud569\ub2c8\ub2e4.<\/li>\n
  4. \ud074\ub7ec\uc2a4\ud130 \ud655\uc7a5\uc744 \ubc18\ubcf5\ud558\uc5ec \ubaa8\ub4e0 \ud3ec\uc778\ud2b8\ub97c \ucc98\ub9ac\ud560 \ub54c\uae4c\uc9c0 \uacc4\uc18d\ud569\ub2c8\ub2e4.<\/li>\n
  5. \ub9c8\uc9c0\ub9c9\uc73c\ub85c \uad70\uc9d1\ud654 \uc911 \uc7a1\uc74c \ud3ec\uc778\ud2b8\ub294 \ubcc4\ub3c4\ub85c \ubd84\ub9ac\ub429\ub2c8\ub2e4.<\/li>\n<\/ol>\n

    4. PyTorch \ubc0f \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac \uc124\uce58<\/h2>\n

    \ub2e4\uc74c\uc73c\ub85c, PyTorch \ubc0f \ud544\uc694\ud55c \ub77c\uc774\ube0c\ub7ec\ub9ac\ub97c \uc124\uce58\ud569\ub2c8\ub2e4.<\/p>\n

    \n        \npip install torch torchvision matplotlib scikit-learn\n        <\/code>\n    <\/pre>\n
    5. \ub370\uc774\ud130 \uc900\ube44<\/h2>\n
    \uc2e4\uc2b5\uc744 \uc704\ud574 \uc0dd\uc131\ub41c \uac00\uc0c1\uc758 \ub370\uc774\ud130 \uc138\ud2b8\ub97c \uc0ac\uc6a9\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
    \n        \nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.datasets import make_moons\n\n# \ub370\uc774\ud130 \uc0dd\uc131\nX, _ = make_moons(n_samples=1000, noise=0.1)\nplt.scatter(X[:, 0], X[:, 1], s=5)\nplt.title(\"Make Moons Dataset\")\nplt.xlabel(\"X1\")\nplt.ylabel(\"X2\")\nplt.show()\n        <\/code>\n    <\/pre>\n
    6. DBSCAN \uc54c\uace0\ub9ac\uc998 \uad6c\ud604<\/h2>\n
    \uc774\uc81c DBSCAN \uc54c\uace0\ub9ac\uc998\uc744 \uad6c\ud604\ud574\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \ud150\uc11c \uc870\uc791\uc744 \ud1b5\ud574 \uc54c\uace0\ub9ac\uc998\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.<\/p>\n
    \n        \nfrom sklearn.cluster import DBSCAN\n\n# DBSCAN \ud074\ub7ec\uc2a4\ud130\ub9c1\ndbscan = DBSCAN(eps=0.1, min_samples=5)\nclusters = dbscan.fit_predict(X)\n\n# \uacb0\uacfc \uc2dc\uac01\ud654\nplt.scatter(X[:, 0], X[:, 1], c=clusters, cmap='rainbow', s=5)\nplt.title(\"DBSCAN Clustering Results\")\nplt.xlabel(\"X1\")\nplt.ylabel(\"X2\")\nplt.show()\n        <\/code>\n    <\/pre>\n
    7. \uacb0\uacfc \ud574\uc11d<\/h2>\n
    \uc704\uc758 \uacb0\uacfc\ub97c \uc0b4\ud3b4\ubcf4\uba74, \ub370\uc774\ud130\uc758 \ubc00\ub3c4\uac00 \ub192\uc740 \ubd80\ubd84\uc5d0\uc11c \ud074\ub7ec\uc2a4\ud130\uac00 \ud615\uc131\ub41c \uac83\uc744 \uc54c \uc218 \uc788\uc2b5\ub2c8\ub2e4.
    \n    DBSCAN\uc740 \ub178\uc774\uc988 \ud3ec\uc778\ud2b8\ub97c \uc798 \uac78\ub7ec\ub0b4\uba70, \ub370\uc774\ud130\uc758 \ubaa8\uc591\uacfc \uad00\uacc4\uc5c6\uc774 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud569\ub2c8\ub2e4.
    \n    \uc774 \uc810\uc740 \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc774 \uac00\uc9c0\ub294 \ud070 \uc7a5\uc810 \uc911 \ud558\ub098\uc785\ub2c8\ub2e4.\n    <\/p>\n
    8. \ubcc0\ud615 \ubc0f \uace0\uae09 \uae30\ubc95<\/h2>\n
    DBSCAN \uc678\uc5d0\ub3c4 \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc5d0\ub294 \ub2e4\uc591\ud55c \ubcc0\ud615\uc774 \uc874\uc7ac\ud569\ub2c8\ub2e4. \uc8fc\uc694 \ubcc0\ud615\uc73c\ub85c\ub294 OPTICS(Ordered Points to Identify the Clustering Structure)\uc640 HDBSCAN(
    \n    Hierarchical Density-Based Spatial Clustering of Applications with Noise) \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.
    \n    \uc774\ub4e4\uc740 \ub354 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \uad6c\uc870\ub97c \ucc98\ub9ac\ud560 \uc218 \uc788\ub3c4\ub85d \uac1c\uc120\ub41c \uc54c\uace0\ub9ac\uc998\uc785\ub2c8\ub2e4.\n    <\/p>\n
    9. \uacb0\ub860<\/h2>\n
    \n        \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d \uae30\ubc95\uc740 \ubcf5\uc7a1\ud55c \ub370\uc774\ud130 \uad6c\uc870\ub97c \uc774\ud574\ud558\uace0 \ud0d0\uc0c9\ud558\ub294 \ub370 \ub9e4\uc6b0 \uc720\uc6a9\ud569\ub2c8\ub2e4.
    \n        \uc774\ubc88 \uac15\uc88c\ub97c \ud1b5\ud574 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc744 \uc218\ud589\ud558\uace0,
    \n        \uc2e4\uc804 \ub370\uc774\ud130\uc5d0 \uc5b4\ub5bb\uac8c \uc801\uc6a9\ud560 \uc218 \uc788\ub294\uc9c0\ub97c \uc774\ud574\ud558\ub294 \ub370 \ub3c4\uc6c0\uc774 \ub418\uc5c8\uae30\ub97c \ubc14\ub78d\ub2c8\ub2e4.
    \n        \uc55e\uc73c\ub85c \ub354 \ub9ce\uc740 \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \uba38\uc2e0\ub7ec\ub2dd \uae30\ubc95\uc744 \ub2e4\ub904\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.\n    <\/p>\n
    10. \ucd94\uac00 \uc790\ub8cc<\/h2>\n
    \n        – DBSCAN \ub17c\ubb38: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise<\/a> 
    \n        – PyTorch \uacf5\uc2dd \ubb38\uc11c:     PyTorch Documentation<\/a> \n<\/p>\n
    <\/body><\/p>\n","protected":false},"excerpt":{"rendered":"
    1. \uc11c\ub860 \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc740 \ub370\uc774\ud130 \ub9c8\uc774\ub2dd\uc758 \uc911\uc694\ud55c \uae30\ubc95 \uc911 \ud558\ub098\ub85c, \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \ubc00\ub3c4\ub97c \uae30\ubc18\uc73c\ub85c \ud074\ub7ec\uc2a4\ud130\ub97c \uc2dd\ubcc4\ud558\ub294 \ubc29\ubc95\uc785\ub2c8\ub2e4. \uc774 \uc54c\uace0\ub9ac\uc998\uc740 \ud2b9\ud788 \ube44\uc120\ud615 \ub370\uc774\ud130 \ud615\ud0dc\ub97c \ub2e4\ub8e8\ub294 \ub370 \uc720\uc6a9\ud558\uba70, \uac01 \ud074\ub7ec\uc2a4\ud130\ub294 \ub370\uc774\ud130 \ud3ec\uc778\ud2b8\uc758 \uace0\ubc00\ub3c4 \uc9c0\uc5ed\uc73c\ub85c \uc815\uc758\ub429\ub2c8\ub2e4. \ubcf8 \uac15\uc88c\uc5d0\uc11c\ub294 PyTorch\ub97c \uc0ac\uc6a9\ud558\uc5ec \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d\uc744 \uad6c\ud604\ud558\ub294 \ubc29\ubc95\uc744 \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc6b0\ub9ac\ub294 \uc8fc\uc694 \uac1c\ub150, \uc54c\uace0\ub9ac\uc998, \uadf8\ub9ac\uace0 \uc2e4\uc81c \uad6c\ud604 \uacfc\uc815\uc744 \ub2e8\uacc4\ubcc4\ub85c … \ub354 \ubcf4\uae30 “\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d”<\/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":[33],"tags":[],"class_list":["post-30048","post","type-post","status-publish","format-standard","hentry","category-33"],"yoast_head":"\n\ub525\ub7ec\ub2dd \ud30c\uc774\ud1a0\uce58 \uac15\uc88c, \ubc00\ub3c4 \uae30\ubc18 \uad70\uc9d1 \ubd84\uc11d - 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