\ub525\ub7ec\ub2dd \uae30\uc220\uc758 \ubc1c\uc804\uacfc \ud568\uaed8 \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \ucc98\ub9ac \uae30\uc220\ub3c4 \uac19\uc774 \ubc1c\uc804\ud574 \uc654\uc2b5\ub2c8\ub2e4. \uadf8 \uc911 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \ub370\uc774\ud130 \ub0b4\uc758 \uc228\uaca8\uc9c4 \ud328\ud134\uc744 \ucc3e\uace0, \uc720\uc0ac\ud55c \ub370\uc774\ud130\ub4e4\uc744 \uadf8\ub8f9\ud654\ud558\ub294 \ub370 \ud544\uc694\ud55c \ub9e4\uc6b0 \uc720\uc6a9\ud55c \uae30\ubc95\uc785\ub2c8\ub2e4. \ubcf8 \uae00\uc5d0\uc11c \uc6b0\ub9ac\ub294 \ud30c\uc774\ud1a0\uce58(PyTorch)\ub97c \ud65c\uc6a9\ud558\uc5ec \ud074\ub7ec\uc2a4\ud130\ub9c1\uc758 \uae30\ucd08\ubd80\ud130 \uace0\uae09 \uae30\ubc95\uae4c\uc9c0 \uae4a\uc774 \uc788\uac8c \ud0d0\uad6c\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
\ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \uc8fc\uc5b4\uc9c4 \ub370\uc774\ud130 \uc9d1\ud569\uc744 \uc720\uc0ac\uc131\uc5d0 \ub530\ub77c \uc5ec\ub7ec \uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\ub85c \ub098\ub204\ub294 \uae30\ubc95\uc785\ub2c8\ub2e4. \uc774 \uacfc\uc815\uc5d0\uc11c \uac01 \ud074\ub7ec\uc2a4\ud130\ub294 \ub0b4\ubd80\uc801\uc73c\ub85c \ub9e4\uc6b0 \uc720\uc0ac\ud55c \ub370\uc774\ud130\ub97c \uac00\uc9c0\uace0 \uc788\uc73c\ub098, \ub2e4\ub978 \ud074\ub7ec\uc2a4\ud130\uc640\ub294 \ud655\uc5f0\ud788 \uad6c\ubd84\ub429\ub2c8\ub2e4. \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998\uc758 \ub300\ud45c\uc801\uc778 \uc608\ub85c\ub294 K-\ud3c9\uade0(K-Means), \uacc4\uce35\uc801 \ud074\ub7ec\uc2a4\ud130\ub9c1(Hierarchical Clustering), DBSCAN \ub4f1\uc774 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \uac00\uc7a5 \ub110\ub9ac \uc0ac\uc6a9\ub418\ub294 \ud074\ub7ec\uc2a4\ud130\ub9c1 \ubc29\ubc95 \uc911 \ud558\ub098\ub85c, \ub370\uc774\ud130\uac00 K\uac1c\uc758 \ud074\ub7ec\uc2a4\ud130\ub85c \ub098\ub258\ub294 \uac83\uc744 \ubaa9\ud45c\ub85c \ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \ub2e4\uc74c\uacfc \uac19\uc740 \ub2e8\uacc4\ub85c \uc218\ud589\ub429\ub2c8\ub2e4:<\/p>\n
\uc774\uc81c K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud558\uc5ec \uad6c\ud604\ud574 \ubcf4\uaca0\uc2b5\ub2c8\ub2e4. \uc544\ub798\ub294 K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc704\ud55c \uae30\ubcf8\uc801\uc778 \ucf54\ub4dc \uc608\uc2dc\uc785\ub2c8\ub2e4.<\/p>\n
import numpy as np\nimport matplotlib.pyplot as plt\n\n# \ub370\uc774\ud130 \uc0dd\uc131\nnp.random.seed(0)\nn_samples = 500\nrandom_data = np.random.rand(n_samples, 2)\nplt.scatter(random_data[:, 0], random_data[:, 1], s=10)\nplt.title(\"Randomly Generated Data\")\nplt.xlabel(\"X-axis\")\nplt.ylabel(\"Y-axis\")\nplt.show()<\/code><\/pre>\n2.2 K-\ud3c9\uade0 \uc54c\uace0\ub9ac\uc998 \uad6c\ud604<\/h3>\nclass KMeans:\n def __init__(self, n_clusters=3, max_iters=100):\n self.n_clusters = n_clusters\n self.max_iters = max_iters\n \n def fit(self, data):\n # \ub79c\ub364 \ucd08\uae30 \uc911\uc2ec \uc120\ud0dd\n self.centroids = data[np.random.choice(data.shape[0], self.n_clusters, replace=False)]\n for i in range(self.max_iters):\n # \ud074\ub7ec\uc2a4\ud130 \ud560\ub2f9\n distances = np.linalg.norm(data[:, np.newaxis] - self.centroids, axis=2)\n self.labels = np.argmin(distances, axis=1)\n # \uc911\uc2ec \uc5c5\ub370\uc774\ud2b8\n new_centroids = np.array([data[self.labels == j].mean(axis=0) for j in range(self.n_clusters)])\n if np.all(self.centroids == new_centroids):\n break\n self.centroids = new_centroids\n\n def predict(self, data):\n distances = np.linalg.norm(data[:, np.newaxis] - self.centroids, axis=2)\n return np.argmin(distances, axis=1)<\/code><\/pre>\n2.3 \ubaa8\ub378 \ud6c8\ub828<\/h3>\n# K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1 \ubaa8\ub378 \ud6c8\ub828\nkmeans = KMeans(n_clusters=3)\nkmeans.fit(random_data)\n\n# \ud074\ub7ec\uc2a4\ud130 \uc2dc\uac01\ud654\nplt.scatter(random_data[:, 0], random_data[:, 1], c=kmeans.labels, s=10)\nplt.scatter(kmeans.centroids[:, 0], kmeans.centroids[:, 1], c='red', s=100, marker='X')\nplt.title(\"K-Means Clustering Result\")\nplt.xlabel(\"X-axis\")\nplt.ylabel(\"Y-axis\")\nplt.show()<\/code><\/pre>\n3. \ud074\ub7ec\uc2a4\ud130\uc758 \ud3c9\uac00<\/h2>\n
\ud074\ub7ec\uc2a4\ud130\ub9c1\uc758 \uacb0\uacfc\ub97c \ud3c9\uac00\ud558\ub294 \uac83\uc740 \ub9e4\uc6b0 \uc911\uc694\ud569\ub2c8\ub2e4. \ub2e4\uc218\uc758 \ud3c9\uac00 \uc9c0\ud45c\uac00 \uc874\uc7ac\ud558\uc9c0\ub9cc, \uc77c\ubc18\uc801\uc73c\ub85c \uc0ac\uc6a9\ub418\ub294 \uba87 \uac00\uc9c0 \uc8fc\uc694 \uc9c0\ud45c\ub294 \ub2e4\uc74c\uacfc \uac19\uc2b5\ub2c8\ub2e4:<\/p>\n
\n- \uc2e4\ub8e8\uc5e3 \uc810\uc218(Silhouette Score)<\/strong>: \ud074\ub7ec\uc2a4\ud130\uc758 \uc751\uc9d1\ub825\uacfc \ubd84\ub9ac\ub3c4\ub97c \ud3c9\uac00\ud569\ub2c8\ub2e4. 1\uc5d0 \uac00\uae4c\uc6b8\uc218\ub85d \uc88b\uc2b5\ub2c8\ub2e4.<\/li>\n
- \uc5e3\uc9c0 \ubc00\ub3c4(Euclidean Distance)<\/strong>: \ud074\ub7ec\uc2a4\ud130\uc758 \ud3c9\uade0 \uac70\ub9ac\ub97c \uce21\uc815\ud558\uc5ec \ud074\ub7ec\uc2a4\ud130\ub9c1\uc758 \ud488\uc9c8\uc744 \ud3c9\uac00\ud569\ub2c8\ub2e4.<\/li>\n<\/ul>\n
3.1 \uc2e4\ub8e8\uc5e3 \uc810\uc218 \uacc4\uc0b0<\/h3>\nfrom sklearn.metrics import silhouette_score\n\n# \uc2e4\ub8e8\uc5e3 \uc810\uc218 \uacc4\uc0b0\nscore = silhouette_score(random_data, kmeans.labels)\nprint(f\"Silhouette Score: {score:.2f}\")<\/code><\/pre>\n4. \uace0\uae09 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uae30\ubc95<\/h2>\n
\uae30\ubcf8\uc801\uc778 K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc678\uc5d0\ub3c4 \ub2e4\uc591\ud55c \uace0\uae09 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uae30\ubc95\uc774 \uac1c\ubc1c\ub418\uc5c8\uc2b5\ub2c8\ub2e4. \uc5ec\uae30\uc11c\ub294 \uadf8 \uc911 \uc77c\ubd80\ub97c \uc0b4\ud3b4\ubcf4\uaca0\uc2b5\ub2c8\ub2e4.<\/p>\n
4.1 DBSCAN<\/h3>\n
DBSCAN(Density-Based Spatial Clustering of Applications with Noise)\uc740 \ubc00\ub3c4 \uae30\ubc18\uc758 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uc54c\uace0\ub9ac\uc998\uc73c\ub85c, \ud074\ub7ec\uc2a4\ud130\uc758 \ubc00\ub3c4\uc5d0 \ub530\ub77c \ud074\ub7ec\uc2a4\ud130\ub97c \uc815\uc758\ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \ub178\uc774\uc988\uc5d0 \ub300\ud55c \uc800\ud56d\ub825\uc774 \ub6f0\uc5b4\ub098\uace0, \ud074\ub7ec\uc2a4\ud130\uc758 \ubaa8\uc591\uc774 \uad6c\ud615\uc774 \uc544\ub2d0 \uacbd\uc6b0\uc5d0\ub3c4 \ud6a8\uacfc\uc801\uc785\ub2c8\ub2e4.<\/p>\n
4.2 \uacc4\uce35\uc801 \ud074\ub7ec\uc2a4\ud130\ub9c1<\/h3>\n
\uacc4\uce35\uc801 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \uacc4\uce35\uc801\uc778 \uad6c\uc870\ub85c \ud074\ub7ec\uc2a4\ud130\ub9c1\uc744 \uc218\ud589\ud569\ub2c8\ub2e4. \uc774 \ubc29\ubc95\uc740 \ud074\ub7ec\uc2a4\ud130 \uac04\uc758 \uc720\uc0ac\uc131\uc744 \uae30\ubc18\uc73c\ub85c \ud074\ub7ec\uc2a4\ud130\ub97c \ubcd1\ud569\ud558\uac70\ub098 \ub098\ub204\ub294 \ubc29\uc2dd\uc73c\ub85c \uc791\uc5c5\ud569\ub2c8\ub2e4. \uacb0\uacfc\uc801\uc73c\ub85c \ub374\ub4dc\ub85c\uadf8\ub7a8(\uacc4\uce35 \uad6c\uc870 \uadf8\ub798\ud504)\uc744 \uc0dd\uc131\ud558\uc5ec \ud074\ub7ec\uc2a4\ud130 \uc218\ub97c \uc2dc\uac01\uc801\uc73c\ub85c \uacb0\uc815\ud560 \uc218 \uc788\uc2b5\ub2c8\ub2e4.<\/p>\n
4.3 \ud30c\uc774\uc36c\uc5d0\uc11c DBSCAN \uad6c\ud604<\/h3>\nfrom sklearn.cluster import DBSCAN\n\n# DBSCAN \ubaa8\ub378 \ud6c8\ub828\ndbscan = DBSCAN(eps=0.3, min_samples=5)\ndbscan_labels = dbscan.fit_predict(random_data)\n\n# DBSCAN \uacb0\uacfc \uc2dc\uac01\ud654\nplt.scatter(random_data[:, 0], random_data[:, 1], c=dbscan_labels, s=10)\nplt.title(\"DBSCAN Clustering Result\")\nplt.xlabel(\"X-axis\")\nplt.ylabel(\"Y-axis\")\nplt.show()<\/code><\/pre>\n5. \ub9c8\ubb34\ub9ac<\/h2>\n
\uc774\ubc88 \uac15\uc88c\uc5d0\uc11c\ub294 \ud30c\uc774\ud1a0\uce58\ub97c \ud65c\uc6a9\ud55c K-\ud3c9\uade0 \ud074\ub7ec\uc2a4\ud130\ub9c1\uc758 \uad6c\ud604\uacfc \ud3c9\uac00 \ubc29\ubc95, \uadf8\ub9ac\uace0 \uace0\uae09 \ud074\ub7ec\uc2a4\ud130\ub9c1 \uae30\ubc95\uc5d0 \ub300\ud574 \uc54c\uc544\ubcf4\uc558\uc2b5\ub2c8\ub2e4. \ud074\ub7ec\uc2a4\ud130\ub9c1\uc740 \ub2e4\uc591\ud55c \ubd84\uc57c\uc5d0\uc11c \ub370\uc774\ud130 \ubd84\uc11d \ubc0f \ucc98\ub9ac\uc758 \uc911\uc694\ud55c \uae30\ubc95 \uc911 \ud558\ub098\uc774\uba70, \uc774\ub97c \ud1b5\ud574 \uc6b0\ub9ac\ub294 \ub370\uc774\ud130\uc758 \uad6c\uc870\uc640 \ud328\ud134\uc744 \ud30c\uc545\ud558\ub294 \ub370 \ub3c4\uc6c0\uc744 \ubc1b\uc744 \uc218 \uc788\uc2b5\ub2c8\ub2e4. \ud5a5\ud6c4 \ub2e4\uc591\ud55c \ud074\ub7ec\uc2a4\ud130\ub9c1 \uae30\uc220\uc744 \ud65c\uc6a9\ud558\uc5ec \uc2e4\uc81c \ub370\uc774\ud130\uc5d0 \uc801\uc6a9\ud574 \ubcf4\ub294 \uac83\uc744 \ucd94\ucc9c\ub4dc\ub9bd\ub2c8\ub2e4.<\/p>\n
\ub525\ub7ec\ub2dd \ubc0f \uba38\uc2e0\ub7ec\ub2dd\uc5d0 \ub300\ud55c \uc9c0\uc18d\uc801\uc778 \uc5f0\uad6c\uc640 \ud559\uc2b5\uc744 \ud1b5\ud574 \ub354 \uae4a\uc774 \uc788\ub294 \ud1b5\ucc30\ub825\uc744 \uc5bb\uae38 \ubc14\ub78d\ub2c8\ub2e4. \uac10\uc0ac\ud569\ub2c8\ub2e4.<\/p>\n
<\/body><\/p>\n","protected":false},"excerpt":{"rendered":"