12_clustering_kmeans.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "c025e329-0d38-4199-bfcd-a672e1105b22",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Clustering"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bba250c4-f81d-4503-b85d-87aef5712278",
   "metadata": {},
   "source": [
    "## 데이터 불러오기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "263bfa25-5fb5-4ff7-aa2a-6b74ef9d6090",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import datasets\n",
    "import pandas as pd\n",
    "iris = datasets.load_iris(as_frame=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0d453ef1-437c-4dfc-a22c-685f74da0ba5",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=iris['data']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5432bb3e-39f2-4a6f-941a-dad2612ef269",
   "metadata": {},
   "source": [
    "## 클러스터링 학습하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "18561598-2379-4c4b-8413-a6ad59fb0663",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KMeans()"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import KMeans\n",
    "kmeans=KMeans()\n",
    "kmeans.fit(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "35c2a29e-7d4b-4111-bc31-9d779c02fbf6",
   "metadata": {},
   "source": [
    "## 중점 확인하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2b623568-debd-4dea-b2f1-f0d15d855451",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.24285714, 3.66785714, 1.5       , 0.28214286],\n",
       "       [6.03684211, 2.70526316, 5.        , 1.77894737],\n",
       "       [5.62857143, 2.72380952, 4.13333333, 1.2952381 ],\n",
       "       [6.61666667, 3.07916667, 5.5625    , 2.1375    ],\n",
       "       [6.44210526, 2.97894737, 4.59473684, 1.43157895],\n",
       "       [4.70454545, 3.12272727, 1.41363636, 0.2       ],\n",
       "       [5.24285714, 2.37142857, 3.44285714, 1.02857143],\n",
       "       [7.54      , 3.15      , 6.39      , 2.09      ]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kmeans.cluster_centers_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7b09f1c5-a7d5-4762-9965-39b8df7e7582",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.110906071998187"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## 중점과 소속된 점들사이 평균거리 확인\n",
    "kmeans.inertia_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "101a2b17-ec9e-4246-b109-28d854807810",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 5, 5, 5, 0, 0, 5, 0, 5, 5, 0, 5, 5, 5, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       5, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 0, 5, 5, 0, 0, 5, 0, 0, 5, 5, 0,\n",
       "       0, 5, 0, 5, 0, 5, 4, 4, 4, 2, 4, 2, 4, 6, 4, 2, 6, 2, 2, 4, 2, 4,\n",
       "       2, 2, 1, 2, 1, 2, 1, 4, 4, 4, 4, 4, 4, 6, 6, 6, 2, 1, 2, 4, 4, 4,\n",
       "       2, 2, 2, 4, 2, 6, 2, 2, 2, 4, 6, 2, 3, 1, 3, 3, 3, 7, 2, 7, 3, 7,\n",
       "       3, 1, 3, 1, 1, 3, 3, 7, 7, 1, 3, 1, 7, 1, 3, 7, 1, 1, 3, 3, 7, 7,\n",
       "       3, 1, 1, 7, 3, 3, 1, 3, 3, 3, 1, 3, 3, 3, 1, 3, 3, 1], dtype=int32)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 분류된 점 확인\n",
    "kmeans.labels_"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da98ca00-e0a3-4002-b16c-d861c3d06376",
   "metadata": {},
   "source": [
    "## K를 바꾸어가며 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b20c06f7-fcef-4e40-aec8-17ec2d40cce9",
   "metadata": {},
   "outputs": [],
   "source": [
    "cn=[]\n",
    "measure=[]\n",
    "for i in range(1,10):\n",
    "    kmeans= KMeans(n_clusters=i)\n",
    "    kmeans.fit(df)\n",
    "    cn.append(i)\n",
    "    measure.append(kmeans.inertia_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4fdb5b10-6042-4dbd-80fa-880b21703ad2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[681.3706,\n",
       " 152.3479517603579,\n",
       " 78.85144142614601,\n",
       " 57.25600931571815,\n",
       " 46.44618205128205,\n",
       " 39.03998724608724,\n",
       " 34.421674242424245,\n",
       " 30.064593073593073,\n",
       " 27.98487343358396]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "measure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "e2c0e126-fc7e-4656-93c1-51e5ee3300e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<BarContainer object of 9 artists>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.figure(figsize=(12,6))\n",
    "plt.plot(cn,measure,'ro-')\n",
    "plt.bar(cn,measure,tick_label=cn)\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
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