AgglomerativeClustering.ipynb

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       "      <th>dm2</th>\n",
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       "      <th>dm5</th>\n",
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       "      <th>dm7</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>운영체제 서비스</td>\n",
       "      <td>-0.005363</td>\n",
       "      <td>-0.000512</td>\n",
       "      <td>0.007147</td>\n",
       "      <td>-0.001610</td>\n",
       "      <td>0.006108</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002862</td>\n",
       "      <td>-0.001767</td>\n",
       "      <td>0.004287</td>\n",
       "      <td>-0.007653</td>\n",
       "      <td>0.001113</td>\n",
       "      <td>0.002021</td>\n",
       "      <td>0.007376</td>\n",
       "      <td>0.001316</td>\n",
       "      <td>-0.001175</td>\n",
       "      <td>0.005756</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>운영체제 사용자 인터페이스 (User Operating System Interface)</td>\n",
       "      <td>운영체제 사용자 인터페이스</td>\n",
       "      <td>-0.000630</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.001856</td>\n",
       "      <td>-0.001423</td>\n",
       "      <td>0.002517</td>\n",
       "      <td>-0.002896</td>\n",
       "      <td>-0.001903</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002282</td>\n",
       "      <td>0.003275</td>\n",
       "      <td>-0.001815</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.003222</td>\n",
       "      <td>0.007290</td>\n",
       "      <td>-0.003162</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>-0.003919</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>시스템 콜 (System Calls)</td>\n",
       "      <td>시스템 콜</td>\n",
       "      <td>-0.003862</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>-0.001253</td>\n",
       "      <td>0.003640</td>\n",
       "      <td>-0.002621</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003519</td>\n",
       "      <td>-0.001859</td>\n",
       "      <td>0.004261</td>\n",
       "      <td>-0.001774</td>\n",
       "      <td>0.006943</td>\n",
       "      <td>-0.001050</td>\n",
       "      <td>-0.000977</td>\n",
       "      <td>-0.001750</td>\n",
       "      <td>0.001026</td>\n",
       "      <td>0.004420</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>시스템 콜 유형 (Types of System Calls)</td>\n",
       "      <td>시스템 콜 유형</td>\n",
       "      <td>-0.002767</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>-0.001221</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>-0.001459</td>\n",
       "      <td>-0.004366</td>\n",
       "      <td>0.003231</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>-0.001968</td>\n",
       "      <td>0.006920</td>\n",
       "      <td>-0.002910</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>-0.002243</td>\n",
       "      <td>-0.000867</td>\n",
       "      <td>0.001493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>시스템 프로그램 (System Programs)</td>\n",
       "      <td>시스템 프로그램</td>\n",
       "      <td>-0.004434</td>\n",
       "      <td>0.005097</td>\n",
       "      <td>0.002258</td>\n",
       "      <td>0.003769</td>\n",
       "      <td>-0.002179</td>\n",
       "      <td>-0.006612</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001859</td>\n",
       "      <td>-0.000062</td>\n",
       "      <td>0.001234</td>\n",
       "      <td>-0.000921</td>\n",
       "      <td>0.010356</td>\n",
       "      <td>0.002786</td>\n",
       "      <td>-0.001200</td>\n",
       "      <td>-0.007887</td>\n",
       "      <td>-0.000642</td>\n",
       "      <td>0.000139</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>341</td>\n",
       "      <td>운영체제를 하드웨어가 시작할 수 있도록 하드웨어에게 알려야 한다</td>\n",
       "      <td>운영체제 하드웨어 시작 수 하드웨어</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>-0.000704</td>\n",
       "      <td>-0.003150</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>-0.006730</td>\n",
       "      <td>-0.001161</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.002351</td>\n",
       "      <td>-0.003470</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>0.002757</td>\n",
       "      <td>-0.004071</td>\n",
       "      <td>-0.003942</td>\n",
       "      <td>-0.002429</td>\n",
       "      <td>0.003343</td>\n",
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       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>342</td>\n",
       "      <td>ROM 또는 EPROM(Erasable Programmable ROM)에 있는 bo...</td>\n",
       "      <td>코드 커널 메모리 적재 커널 구동</td>\n",
       "      <td>0.005307</td>\n",
       "      <td>-0.001297</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.000625</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>-0.003978</td>\n",
       "      <td>0.004232</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>-0.003719</td>\n",
       "      <td>-0.004412</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.003744</td>\n",
       "      <td>-0.001741</td>\n",
       "      <td>-0.005637</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>-0.006125</td>\n",
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       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>343</td>\n",
       "      <td>두 단계로 나눠지는 경우도 있음- ROM의 특정한 위치에 있는부트블록(boot bl...</td>\n",
       "      <td>두 단계 경우 의 위치 부트 블록 이 메모리 로드 것 디스크 부트스트랩 로더 메모리 적재</td>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.001371</td>\n",
       "      <td>0.001448</td>\n",
       "      <td>-0.001155</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>-0.002269</td>\n",
       "      <td>-0.000866</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003081</td>\n",
       "      <td>-0.002327</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>-0.000070</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>-0.001879</td>\n",
       "      <td>-0.000852</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>-0.001587</td>\n",
       "      <td>-0.002540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>344</td>\n",
       "      <td>GRUB(GRand Unified Bootloader) : Linux 시스템을 위...</td>\n",
       "      <td>시스템 위 오픈소스 부트스트랩 프로그램</td>\n",
       "      <td>-0.001683</td>\n",
       "      <td>0.003580</td>\n",
       "      <td>-0.001254</td>\n",
       "      <td>0.005246</td>\n",
       "      <td>0.001063</td>\n",
       "      <td>-0.003277</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002042</td>\n",
       "      <td>0.003508</td>\n",
       "      <td>-0.003454</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>-0.000311</td>\n",
       "      <td>-0.001045</td>\n",
       "      <td>-0.008586</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>-0.001097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>345</td>\n",
       "      <td>커널이 적재되어 시스템이 실행(running)되게 된다</td>\n",
       "      <td>커널 적재 시스템 실행</td>\n",
       "      <td>-0.001806</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.005178</td>\n",
       "      <td>0.005262</td>\n",
       "      <td>-0.004082</td>\n",
       "      <td>-0.004182</td>\n",
       "      <td>0.007642</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>-0.000610</td>\n",
       "      <td>0.001566</td>\n",
       "      <td>0.006023</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>-0.003499</td>\n",
       "      <td>0.004368</td>\n",
       "      <td>-0.003461</td>\n",
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       "<p>346 rows × 103 columns</p>\n",
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       "     Unnamed: 0                                           Document  \\\n",
       "0             0               운영체제 서비스 (Operating System Services)   \n",
       "1             1   운영체제 사용자 인터페이스 (User Operating System Interface)   \n",
       "2             2                               시스템 콜 (System Calls)   \n",
       "3             3                   시스템 콜 유형 (Types of System Calls)   \n",
       "4             4                         시스템 프로그램 (System Programs)   \n",
       "..          ...                                                ...   \n",
       "341         341                운영체제를 하드웨어가 시작할 수 있도록 하드웨어에게 알려야 한다   \n",
       "342         342  ROM 또는 EPROM(Erasable Programmable ROM)에 있는 bo...   \n",
       "343         343  두 단계로 나눠지는 경우도 있음- ROM의 특정한 위치에 있는부트블록(boot bl...   \n",
       "344         344   GRUB(GRand Unified Bootloader) : Linux 시스템을 위...   \n",
       "345         345                    커널이 적재되어 시스템이 실행(running)되게 된다    \n",
       "\n",
       "                                     tokenizedDocument       dm1       dm2  \\\n",
       "0                                             운영체제 서비스 -0.005363 -0.000512   \n",
       "1                                       운영체제 사용자 인터페이스 -0.000630  0.002080   \n",
       "2                                                시스템 콜 -0.003862  0.001073   \n",
       "3                                             시스템 콜 유형 -0.002767  0.001186   \n",
       "4                                             시스템 프로그램 -0.004434  0.005097   \n",
       "..                                                 ...       ...       ...   \n",
       "341                                운영체제 하드웨어 시작 수 하드웨어  0.000650  0.000841   \n",
       "342                                 코드 커널 메모리 적재 커널 구동  0.005307 -0.001297   \n",
       "343  두 단계 경우 의 위치 부트 블록 이 메모리 로드 것 디스크 부트스트랩 로더 메모리 적재  0.001275  0.001371   \n",
       "344                              시스템 위 오픈소스 부트스트랩 프로그램 -0.001683  0.003580   \n",
       "345                                       커널 적재 시스템 실행 -0.001806  0.001183   \n",
       "\n",
       "          dm3       dm4       dm5       dm6       dm7  ...      dm91  \\\n",
       "0    0.007147 -0.001610  0.006108 -0.001265  0.000054  ...  0.002862   \n",
       "1    0.001856 -0.001423  0.002517 -0.002896 -0.001903  ...  0.002500   \n",
       "2   -0.001253  0.003640 -0.002621 -0.001705  0.004142  ...  0.003519   \n",
       "3   -0.001221  0.001078 -0.001459 -0.004366  0.003231  ...  0.005221   \n",
       "4    0.002258  0.003769 -0.002179 -0.006612  0.004854  ... -0.001859   \n",
       "..        ...       ...       ...       ...       ...  ...       ...   \n",
       "341 -0.000704 -0.003150  0.001503 -0.006730 -0.001161  ...  0.002095   \n",
       "342  0.001391  0.000625 -0.000349 -0.003978  0.004232  ...  0.000073   \n",
       "343  0.001448 -0.001155  0.000650 -0.002269 -0.000866  ...  0.003081   \n",
       "344 -0.001254  0.005246  0.001063 -0.003277  0.001220  ...  0.002042   \n",
       "345  0.005178  0.005262 -0.004082 -0.004182  0.007642  ...  0.000803   \n",
       "\n",
       "         dm92      dm93      dm94      dm95      dm96      dm97      dm98  \\\n",
       "0   -0.001767  0.004287 -0.007653  0.001113  0.002021  0.007376  0.001316   \n",
       "1    0.002282  0.003275 -0.001815  0.000834  0.003222  0.007290 -0.003162   \n",
       "2   -0.001859  0.004261 -0.001774  0.006943 -0.001050 -0.000977 -0.001750   \n",
       "3   -0.000273  0.001127 -0.001968  0.006920 -0.002910  0.002440 -0.002243   \n",
       "4   -0.000062  0.001234 -0.000921  0.010356  0.002786 -0.001200 -0.007887   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "341  0.001415  0.002351 -0.003470  0.001670  0.002757 -0.004071 -0.003942   \n",
       "342 -0.003719 -0.004412  0.002500  0.000399 -0.003744 -0.001741 -0.005637   \n",
       "343 -0.002327  0.000382 -0.000070  0.001456 -0.001879 -0.000852 -0.002582   \n",
       "344  0.003508 -0.003454  0.000900  0.004378 -0.000311 -0.001045 -0.008586   \n",
       "345 -0.002415 -0.000610  0.001566  0.006023  0.001235  0.000622 -0.003499   \n",
       "\n",
       "         dm99     dm100  \n",
       "0   -0.001175  0.005756  \n",
       "1    0.001100 -0.003919  \n",
       "2    0.001026  0.004420  \n",
       "3   -0.000867  0.001493  \n",
       "4   -0.000642  0.000139  \n",
       "..        ...       ...  \n",
       "341 -0.002429  0.003343  \n",
       "342  0.000676 -0.006125  \n",
       "343 -0.001587 -0.002540  \n",
       "344  0.003327 -0.001097  \n",
       "345  0.004368 -0.003461  \n",
       "\n",
       "[346 rows x 103 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "# scikit-learn을 이용한 병합 클러스터링\n",
    "\n",
    "X = pd.read_csv('final.csv')\n",
    "\n",
    "x=X.iloc[:, 3:103]\n",
    "y=X.iloc[:,[1]]\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "77ddee50",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>dm1</th>\n",
       "      <th>dm2</th>\n",
       "      <th>dm3</th>\n",
       "      <th>dm4</th>\n",
       "      <th>dm5</th>\n",
       "      <th>dm6</th>\n",
       "      <th>dm7</th>\n",
       "      <th>dm8</th>\n",
       "      <th>dm9</th>\n",
       "      <th>dm10</th>\n",
       "      <th>...</th>\n",
       "      <th>dm91</th>\n",
       "      <th>dm92</th>\n",
       "      <th>dm93</th>\n",
       "      <th>dm94</th>\n",
       "      <th>dm95</th>\n",
       "      <th>dm96</th>\n",
       "      <th>dm97</th>\n",
       "      <th>dm98</th>\n",
       "      <th>dm99</th>\n",
       "      <th>dm100</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-0.005363</td>\n",
       "      <td>-0.000512</td>\n",
       "      <td>0.007147</td>\n",
       "      <td>-0.001610</td>\n",
       "      <td>0.006108</td>\n",
       "      <td>-0.001265</td>\n",
       "      <td>0.000054</td>\n",
       "      <td>0.008724</td>\n",
       "      <td>0.002953</td>\n",
       "      <td>-0.006172</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002862</td>\n",
       "      <td>-0.001767</td>\n",
       "      <td>0.004287</td>\n",
       "      <td>-0.007653</td>\n",
       "      <td>0.001113</td>\n",
       "      <td>0.002021</td>\n",
       "      <td>0.007376</td>\n",
       "      <td>0.001316</td>\n",
       "      <td>-0.001175</td>\n",
       "      <td>0.005756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-0.000630</td>\n",
       "      <td>0.002080</td>\n",
       "      <td>0.001856</td>\n",
       "      <td>-0.001423</td>\n",
       "      <td>0.002517</td>\n",
       "      <td>-0.002896</td>\n",
       "      <td>-0.001903</td>\n",
       "      <td>0.006140</td>\n",
       "      <td>-0.005848</td>\n",
       "      <td>-0.003149</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.002282</td>\n",
       "      <td>0.003275</td>\n",
       "      <td>-0.001815</td>\n",
       "      <td>0.000834</td>\n",
       "      <td>0.003222</td>\n",
       "      <td>0.007290</td>\n",
       "      <td>-0.003162</td>\n",
       "      <td>0.001100</td>\n",
       "      <td>-0.003919</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.003862</td>\n",
       "      <td>0.001073</td>\n",
       "      <td>-0.001253</td>\n",
       "      <td>0.003640</td>\n",
       "      <td>-0.002621</td>\n",
       "      <td>-0.001705</td>\n",
       "      <td>0.004142</td>\n",
       "      <td>0.006940</td>\n",
       "      <td>-0.005425</td>\n",
       "      <td>0.001427</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003519</td>\n",
       "      <td>-0.001859</td>\n",
       "      <td>0.004261</td>\n",
       "      <td>-0.001774</td>\n",
       "      <td>0.006943</td>\n",
       "      <td>-0.001050</td>\n",
       "      <td>-0.000977</td>\n",
       "      <td>-0.001750</td>\n",
       "      <td>0.001026</td>\n",
       "      <td>0.004420</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.002767</td>\n",
       "      <td>0.001186</td>\n",
       "      <td>-0.001221</td>\n",
       "      <td>0.001078</td>\n",
       "      <td>-0.001459</td>\n",
       "      <td>-0.004366</td>\n",
       "      <td>0.003231</td>\n",
       "      <td>0.003249</td>\n",
       "      <td>-0.005552</td>\n",
       "      <td>0.002219</td>\n",
       "      <td>...</td>\n",
       "      <td>0.005221</td>\n",
       "      <td>-0.000273</td>\n",
       "      <td>0.001127</td>\n",
       "      <td>-0.001968</td>\n",
       "      <td>0.006920</td>\n",
       "      <td>-0.002910</td>\n",
       "      <td>0.002440</td>\n",
       "      <td>-0.002243</td>\n",
       "      <td>-0.000867</td>\n",
       "      <td>0.001493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.004434</td>\n",
       "      <td>0.005097</td>\n",
       "      <td>0.002258</td>\n",
       "      <td>0.003769</td>\n",
       "      <td>-0.002179</td>\n",
       "      <td>-0.006612</td>\n",
       "      <td>0.004854</td>\n",
       "      <td>0.009221</td>\n",
       "      <td>-0.000331</td>\n",
       "      <td>-0.005905</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001859</td>\n",
       "      <td>-0.000062</td>\n",
       "      <td>0.001234</td>\n",
       "      <td>-0.000921</td>\n",
       "      <td>0.010356</td>\n",
       "      <td>0.002786</td>\n",
       "      <td>-0.001200</td>\n",
       "      <td>-0.007887</td>\n",
       "      <td>-0.000642</td>\n",
       "      <td>0.000139</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>0.000650</td>\n",
       "      <td>0.000841</td>\n",
       "      <td>-0.000704</td>\n",
       "      <td>-0.003150</td>\n",
       "      <td>0.001503</td>\n",
       "      <td>-0.006730</td>\n",
       "      <td>-0.001161</td>\n",
       "      <td>0.007772</td>\n",
       "      <td>0.003788</td>\n",
       "      <td>-0.006122</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002095</td>\n",
       "      <td>0.001415</td>\n",
       "      <td>0.002351</td>\n",
       "      <td>-0.003470</td>\n",
       "      <td>0.001670</td>\n",
       "      <td>0.002757</td>\n",
       "      <td>-0.004071</td>\n",
       "      <td>-0.003942</td>\n",
       "      <td>-0.002429</td>\n",
       "      <td>0.003343</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>0.005307</td>\n",
       "      <td>-0.001297</td>\n",
       "      <td>0.001391</td>\n",
       "      <td>0.000625</td>\n",
       "      <td>-0.000349</td>\n",
       "      <td>-0.003978</td>\n",
       "      <td>0.004232</td>\n",
       "      <td>0.001470</td>\n",
       "      <td>-0.003387</td>\n",
       "      <td>-0.001689</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000073</td>\n",
       "      <td>-0.003719</td>\n",
       "      <td>-0.004412</td>\n",
       "      <td>0.002500</td>\n",
       "      <td>0.000399</td>\n",
       "      <td>-0.003744</td>\n",
       "      <td>-0.001741</td>\n",
       "      <td>-0.005637</td>\n",
       "      <td>0.000676</td>\n",
       "      <td>-0.006125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>0.001275</td>\n",
       "      <td>0.001371</td>\n",
       "      <td>0.001448</td>\n",
       "      <td>-0.001155</td>\n",
       "      <td>0.000650</td>\n",
       "      <td>-0.002269</td>\n",
       "      <td>-0.000866</td>\n",
       "      <td>0.005654</td>\n",
       "      <td>-0.000950</td>\n",
       "      <td>0.000396</td>\n",
       "      <td>...</td>\n",
       "      <td>0.003081</td>\n",
       "      <td>-0.002327</td>\n",
       "      <td>0.000382</td>\n",
       "      <td>-0.000070</td>\n",
       "      <td>0.001456</td>\n",
       "      <td>-0.001879</td>\n",
       "      <td>-0.000852</td>\n",
       "      <td>-0.002582</td>\n",
       "      <td>-0.001587</td>\n",
       "      <td>-0.002540</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>-0.001683</td>\n",
       "      <td>0.003580</td>\n",
       "      <td>-0.001254</td>\n",
       "      <td>0.005246</td>\n",
       "      <td>0.001063</td>\n",
       "      <td>-0.003277</td>\n",
       "      <td>0.001220</td>\n",
       "      <td>0.003984</td>\n",
       "      <td>0.003013</td>\n",
       "      <td>-0.001263</td>\n",
       "      <td>...</td>\n",
       "      <td>0.002042</td>\n",
       "      <td>0.003508</td>\n",
       "      <td>-0.003454</td>\n",
       "      <td>0.000900</td>\n",
       "      <td>0.004378</td>\n",
       "      <td>-0.000311</td>\n",
       "      <td>-0.001045</td>\n",
       "      <td>-0.008586</td>\n",
       "      <td>0.003327</td>\n",
       "      <td>-0.001097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>-0.001806</td>\n",
       "      <td>0.001183</td>\n",
       "      <td>0.005178</td>\n",
       "      <td>0.005262</td>\n",
       "      <td>-0.004082</td>\n",
       "      <td>-0.004182</td>\n",
       "      <td>0.007642</td>\n",
       "      <td>0.002683</td>\n",
       "      <td>-0.006364</td>\n",
       "      <td>-0.003908</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000803</td>\n",
       "      <td>-0.002415</td>\n",
       "      <td>-0.000610</td>\n",
       "      <td>0.001566</td>\n",
       "      <td>0.006023</td>\n",
       "      <td>0.001235</td>\n",
       "      <td>0.000622</td>\n",
       "      <td>-0.003499</td>\n",
       "      <td>0.004368</td>\n",
       "      <td>-0.003461</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 100 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          dm1       dm2       dm3       dm4       dm5       dm6       dm7  \\\n",
       "0   -0.005363 -0.000512  0.007147 -0.001610  0.006108 -0.001265  0.000054   \n",
       "1   -0.000630  0.002080  0.001856 -0.001423  0.002517 -0.002896 -0.001903   \n",
       "2   -0.003862  0.001073 -0.001253  0.003640 -0.002621 -0.001705  0.004142   \n",
       "3   -0.002767  0.001186 -0.001221  0.001078 -0.001459 -0.004366  0.003231   \n",
       "4   -0.004434  0.005097  0.002258  0.003769 -0.002179 -0.006612  0.004854   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "341  0.000650  0.000841 -0.000704 -0.003150  0.001503 -0.006730 -0.001161   \n",
       "342  0.005307 -0.001297  0.001391  0.000625 -0.000349 -0.003978  0.004232   \n",
       "343  0.001275  0.001371  0.001448 -0.001155  0.000650 -0.002269 -0.000866   \n",
       "344 -0.001683  0.003580 -0.001254  0.005246  0.001063 -0.003277  0.001220   \n",
       "345 -0.001806  0.001183  0.005178  0.005262 -0.004082 -0.004182  0.007642   \n",
       "\n",
       "          dm8       dm9      dm10  ...      dm91      dm92      dm93  \\\n",
       "0    0.008724  0.002953 -0.006172  ...  0.002862 -0.001767  0.004287   \n",
       "1    0.006140 -0.005848 -0.003149  ...  0.002500  0.002282  0.003275   \n",
       "2    0.006940 -0.005425  0.001427  ...  0.003519 -0.001859  0.004261   \n",
       "3    0.003249 -0.005552  0.002219  ...  0.005221 -0.000273  0.001127   \n",
       "4    0.009221 -0.000331 -0.005905  ... -0.001859 -0.000062  0.001234   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "341  0.007772  0.003788 -0.006122  ...  0.002095  0.001415  0.002351   \n",
       "342  0.001470 -0.003387 -0.001689  ...  0.000073 -0.003719 -0.004412   \n",
       "343  0.005654 -0.000950  0.000396  ...  0.003081 -0.002327  0.000382   \n",
       "344  0.003984  0.003013 -0.001263  ...  0.002042  0.003508 -0.003454   \n",
       "345  0.002683 -0.006364 -0.003908  ...  0.000803 -0.002415 -0.000610   \n",
       "\n",
       "         dm94      dm95      dm96      dm97      dm98      dm99     dm100  \n",
       "0   -0.007653  0.001113  0.002021  0.007376  0.001316 -0.001175  0.005756  \n",
       "1   -0.001815  0.000834  0.003222  0.007290 -0.003162  0.001100 -0.003919  \n",
       "2   -0.001774  0.006943 -0.001050 -0.000977 -0.001750  0.001026  0.004420  \n",
       "3   -0.001968  0.006920 -0.002910  0.002440 -0.002243 -0.000867  0.001493  \n",
       "4   -0.000921  0.010356  0.002786 -0.001200 -0.007887 -0.000642  0.000139  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "341 -0.003470  0.001670  0.002757 -0.004071 -0.003942 -0.002429  0.003343  \n",
       "342  0.002500  0.000399 -0.003744 -0.001741 -0.005637  0.000676 -0.006125  \n",
       "343 -0.000070  0.001456 -0.001879 -0.000852 -0.002582 -0.001587 -0.002540  \n",
       "344  0.000900  0.004378 -0.000311 -0.001045 -0.008586  0.003327 -0.001097  \n",
       "345  0.001566  0.006023  0.001235  0.000622 -0.003499  0.004368 -0.003461  \n",
       "\n",
       "[346 rows x 100 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "24051f43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Document</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>운영체제 사용자 인터페이스 (User Operating System Interface)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>시스템 콜 (System Calls)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>시스템 콜 유형 (Types of System Calls)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>시스템 프로그램 (System Programs)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>운영체제를 하드웨어가 시작할 수 있도록 하드웨어에게 알려야 한다</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>ROM 또는 EPROM(Erasable Programmable ROM)에 있는 bo...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>두 단계로 나눠지는 경우도 있음- ROM의 특정한 위치에 있는부트블록(boot bl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>GRUB(GRand Unified Bootloader) : Linux 시스템을 위...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>커널이 적재되어 시스템이 실행(running)되게 된다</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>346 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Document\n",
       "0                 운영체제 서비스 (Operating System Services)\n",
       "1     운영체제 사용자 인터페이스 (User Operating System Interface)\n",
       "2                                 시스템 콜 (System Calls)\n",
       "3                     시스템 콜 유형 (Types of System Calls)\n",
       "4                           시스템 프로그램 (System Programs)\n",
       "..                                                 ...\n",
       "341                운영체제를 하드웨어가 시작할 수 있도록 하드웨어에게 알려야 한다\n",
       "342  ROM 또는 EPROM(Erasable Programmable ROM)에 있는 bo...\n",
       "343  두 단계로 나눠지는 경우도 있음- ROM의 특정한 위치에 있는부트블록(boot bl...\n",
       "344   GRUB(GRand Unified Bootloader) : Linux 시스템을 위...\n",
       "345                    커널이 적재되어 시스템이 실행(running)되게 된다 \n",
       "\n",
       "[346 rows x 1 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ab73cb7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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       "       3, 1, 1, 1, 1, 1, 3, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 3, 1, 1, 1, 1,\n",
       "       1, 0, 3, 0, 1, 1, 1, 1, 1, 3, 0, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1,\n",
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       "       0, 3, 1, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1,\n",
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       "       1, 1, 1, 1, 1, 1, 1, 1, 4, 1, 1, 1, 4, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 1, 0, 0], dtype=int64)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=5,linkage=\"ward\")\n",
    "agg = agg.fit(x)\n",
    "re=agg.labels_\n",
    "re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dab79bf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\Anaconda3\\envs\\py37\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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       "..                                                 ...  ..\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>운영체제 서비스 : View</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>백그라운드 서비스 (Background services)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>221</th>\n",
       "      <td>백그라운드 서비스 (Background Service)</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Document  re\n",
       "0      운영체제 서비스 (Operating System Services)   3\n",
       "14                          운영체제 서비스 : View   3\n",
       "17   운영체제 서비스 (Operating System Services)     3\n",
       "22   운영체제 서비스 (Operating System Services)     3\n",
       "28   운영체제 서비스 (Operating System Services)     3\n",
       "39   운영체제 서비스 (Operating System Services)     3\n",
       "46   운영체제 서비스 (Operating System Services)     3\n",
       "53   운영체제 서비스 (Operating System Services)     3\n",
       "200        백그라운드 서비스 (Background services)    3\n",
       "221          백그라운드 서비스 (Background Service)   3"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[y['re']==3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "19b4e1d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Document</th>\n",
       "      <th>re</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>운영체제 구조 (Operating System Structure)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>261</th>\n",
       "      <td>운영체제 구조 (structure)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>264</th>\n",
       "      <td>간단한 구조 -- MS-DOS</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>268</th>\n",
       "      <td>좀더 복잡한 구조 - UNIX</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>271</th>\n",
       "      <td>최초 UNIX는 제한적인 구조를 가졌다</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>284</th>\n",
       "      <td>마이크로 커널 시스템 구조 (Microkerne</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>296</th>\n",
       "      <td>마이크로커널 시스템 구조ApplicationProgramFileSystemDevic...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>316</th>\n",
       "      <td>다른 CPU 구조에서도 실행된다 (ARM 대 Intel)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>320</th>\n",
       "      <td>Android Architecture (구조)</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "                                              Document  re\n",
       "6                 운영체제 구조 (Operating System Structure)   4\n",
       "261                                운영체제 구조 (structure)   4\n",
       "264                                   간단한 구조 -- MS-DOS   4\n",
       "268                                   좀더 복잡한 구조 - UNIX   4\n",
       "271                             최초 UNIX는 제한적인 구조를 가졌다    4\n",
       "284                         마이크로 커널 시스템 구조 (Microkerne   4\n",
       "296  마이크로커널 시스템 구조ApplicationProgramFileSystemDevic...   4\n",
       "316                    다른 CPU 구조에서도 실행된다 (ARM 대 Intel)   4\n",
       "320                          Android Architecture (구조)   4"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[y['re']==4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "15dcc94a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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p3XTTTWn16tWjn5tp9u/rPfbYY2nDhg2V8brjjjsqw1TtqxOtrtXixYvTW97ylvTWt741/fu//3vTf3vbbbellStXtrxeOWnn1ltvTYsWLUp77rlnOvjgg9O9997b2QH8jzPPPDMtWbIkfeUrX0kHHHBA02WZ6q/51Vdf3fQ8joQZHh5umS7Ga3YOR9LKjTfemK6++uqGtHPrrbemAw44IOu4c/PFiJx09vOf/7zIdiYTJjcfV12Dn/70p+nOO+/MKg/OPvvshu/OPPPMdOihh6bTTz89veMd70gf+chHmv7bTq5DuzjfeuutbfPNSJgS5WXOdqric8opp6S3ve1to3mr2fn51re+Nfrfn//5n6dVq1Y1hKnPo3vuuWf68Ic/3DJOKT2dPiYqt+zOuRb1muX1TsM0+/26665L11133ej5u+666xrC3Hzzzem//uu/0gEHHJDe+MY3pptvvrkhzJo1a9J//ud/psMOOyy9+c1vTmvWrGkah5x0MSKnDGxVb/2f//N/0re//e20aNGitHjx4nTqqac2Dffkk0+m9evXp02bNjXNo48//nj6wQ9+0Lbs3m233dJFF12UfvWrX6WUUvrlL3/ZNs456SunPk6pdRn305/+tG0Zt3r16vSRj3wk3XzzzWnRokXp61//est9lGhDdFKGtTs/nbbBmsV79erV6bjjjmt77N/85jfTsmXL0s0335ze+MY3tj0/KTU/9tWrV6dly5alW265JR1wwAGV20ipeZq/+uqrR7ez//77N91O/b7233//dNlllzWEqc+jBx98cPr7v//7pnGoakOMlAcj22lWHuS0eUZUpa+qtJxSdZtwvGb7O/PMM9Nhhx3Wtr4566yz0mGHHZYuuOCCtOeeezYNM9KO3XfffdO73vWutm2sqmOvKi/r65tm16qZZvVA7vXKuRb1qsqNVvu56aab0tKlS9Ptt9+ebrnllvSlL32pcl9nnXVWw3dVfYEbb7wxve9970tr165NKT29bGeVdn2XnGueK7e/1Sw+E+k/55TxtVqt4bucNk9ufEr07Ubq7HZ9qZztdHoOJ9NuHC+nvddKp/22dlqVT6tXrx7Tbrz88ssbwpQ6f53cD6mXcw6bpefxqvJFTr7JiUupfu9E+wrXXXddWr16dVq5cmV6y1ve0rQv8I//+I9pr732Gi0vv/jFL7bdz8MPP9y0XL7xxhvT0qVLR7fz2c9+tiFMp+mnVfvzscceG/O5WXxKlWGdtHtGtLtenZQpn/nMZ8Z8rmrHNQuXk79y+iY5YaqOrapt9Oijj1bmh5L3P1uFyT1/VX2XqaiT2h1Xqb541XEtX748XX/99WnPPfdMixYtSuedd15DmK985Svp+uuvT4sWLUoHHnhgy/56StXXvZP7cik1lqkj9dq3vvWt9Na3vjV961vfytpOu/uxrcqVkXsHP/nJT9KmTZuabnf8/YVmSpU7v/71r4v0A0qm05L3PztpF7W6V10qPiNKlJW5+0qpfRu+qq5IqbpMrSoPXvWqV6WzzjqrZXo+88wz0yGHHJJOO+20tvfF6+Uc+2T6U/Um2k4b30fMue7NtlNfHjz88MMt/21VOZibR3PvuU02XUyknd+qb1J13+3mm29O9913X/qzP/uztGjRoqb3sMZrlS46vZffLM5f+cpXRu9XV9V/I6byWVqnfciqPNMqbZR67rn77ruP5olbbrllQnEZr1X+zKmPx99LPPLIIyccnxLP7SbSPmh3vdqNX0ipOv2ccsoplecnJ8433nhjOuKII9J3vvOdtGnTpqb3CZcvXz4mbzVrfy5fvjxdd9116Y1vfGPLNupIn3XdunUppdR0X+PzcbPt1JtovkmpzLOQ+v20Kk/q2+ZvfOMbWx5TJ22+Un3IVmHG17V33nlnQ5ic+Oa0izqtt6qOq921qFeqPVOqjdoqzNVXXz3medFVV13VdjvN2rrXXHNNOv3009M111zT8jl1Snn3fkfK7/vvv79ln6sqPinlP5dqVw5OxX36Uv2J3HtP7dJXu776yDPoqueaI2He/OY3t32GOtn7QTfffHP6j//4j7ZtwjVr1lQ++8x5jrhw4cIxbYhm7fjx90jH1zfjx25cccUVDdsYCXPNNdekN77xjU3zzS233JL22muvts+ubrvttrR48eK05557pj333DPr+dZE02mpsZ8plR9vklJ1nyyllNavX9/033bSrm51fm6++eYxddttt93WECZn3ErOvZ6c9vlIGrvuuutals2dtj8nU9/ceOON6cgjj0y33357yzZhTp19/vnnpzPPPLOy3TjS3mvVNnriiSfS3XffPfq52Tija665Jt11113p0EMPbVmupJTSpk2b2o5XqtfqHHfSbmx3Harqv07uKY2Y6HPPnOuZ29esum+SM+aiVL0+vr5pVcb/4z/+Y1q1alXL53arV69Oe+21V9u6tn4sxMMPP5wuvPDChjBXX331mDop5z5FVTqt8sQTT6Rbbrml5XamYizleHNyJYx99903XvGKV8Rf/MVfxIEHHhiDg4MNYZYtWxY/+clP4qUvfWl88pOfjOOOOy4uuOCC0d+PPfbYePnLXx4vetGL4nvf+15cfvnlY37PDRMR8dnPfjbWr18fP/nJT+LZz3527LrrrrF06dKOj+uII46IhQsXxr/8y7/E/vvvH+9///vj61//+pgwJ5xwQmy77bax3XbbxXOe85xYunTpmKVzjj322Ni8eXMMDg7Gk08+GfPmzWtYzuXMM8+MF77whfHNb34ztt1223jOc54Tp5122pgwBx98cMybNy9SSnHXXXfFrrvu2rDkS86+cixbtizuv//+eNnLXtb0WkVEfPWrX42VK1fGJz7xifjoRz8aRx99dFx22WUN8dlll13ixS9+ccvrlZN2vvrVr8bf/d3ftd1Xjvvuuy9WrFgRf/7nfx7/8A//EMccc0xDmJxrnpsu7r///pbpfSRMu7Tz1a9+NVatWhUnn3xynHDCCS2POydfbNq0afTvlFJ8/OMfjy996Uttw3ziE5+oDFO1nYjI2lezMFX5ODe9V23nkEMOGROXu+66Kz784Q+P2cZ9990Xl156adu0k1uGVcW5VFnYzTD3339/rF69uu35Oemkk+Kggw6K5z3vefH444/H0NBQQ5j6PHrTTTe1PM8lyrmq/Jd77Dl5vapMzdnGxz72sTHnb/369Q3HtHLlyti0aVOcf/75sc0228Rhhx3WMNt21apVsWnTpjj77LNHw4x/A3SptJxTb/3whz+MH/7wh3HVVVdFRMQHPvCBhuPKyaM55fIuu+wSO+ywQxx11FHxrGc9Kw4//PCGt03nHNdIfNodV4kyLiLikksuieXLl8fee+8dt99+exx11FGx3377jQmT04bIiXOp655zXDn1RM6xX3zxxZVhqtJhzn5yjv3LX/5y5Xaa7eud73znmDBXXHFFDA0NxVlnnRXbbrttHHbYYfH2t7+9IT5VZdhIeVC/nfHlQU6+yUlfOdc8p02Yky7uu+++WL58edv65t577x0Nc9NNNzVdHS2nHZtz7Ln1RFV9k1MP5FyvTvNfq/IpJ6+fe+658eUvfzlOO+202HfffWPdunUN57lZ+T3+TQhVfYHzzjtvdD+//OUv4/7772/YT875y7nmOXLOTU58cvpAOXli/L6OP/74CaXBnPiU6tvlpOWc7eTGuVS7seqa5si9n1Glqnw65ZRTxrQbh4eHG7ZR6vzlpK+RcDn946r0XJUvcsq4nLiU6tNW3QfLDVPfF9i4cWPTvsDFF18cV1555Wh5+b3vfa8hTE65PL7c/fGPf9ywnZz0k9MXeN/73lcZn1JlWIn7OLllSrPz/PGPf7yjY8oNl9POL3U/sarsqf/92c9+djz72c9uaDuV6rvkHFPO+ctpO5Wqk3KvQ7f6ZN/97ncjIuKmm26KiOZ98bvuuivuuuuutv31nOteoo1aXw4+8cQT8fOf/7whLhF5ZWpV2hi5d7Bs2bKW9w5y7i+Uun/87ne/u0g/ICeddvPewUTuZze7V93Ne1i5x9Vpf2GibfhS/fVXvepVsfPOO7dMz/fdd19cfvnlbfvhU3l+xueLUs8nmvURX/Oa17TcTqt95ZQHpfJoznZKlcs5aTDn/OTcdxu5h/XVr341tt1221iyZEnDPayc8rLTciWiedrIqf86fZbWal9V57DU84mI6rRRqt562cteNiZP9Pf3N+SJUs/Ec/Jfzr3EUvcAc8LknOecvJWzndxne1XnJ2df9f3IjRs3Nr1P+N3vfje++93vts1bI23UG2+8sWWY+n098sgjTfdVlY8nkm+apY1S92Jzyu6csimiuj7JyVs5bcKcMDn3Y3Pqv9z76yX6zznXotT94VLPI3Ou6bXXXhtXX331mOdFixYt6ig+n/jEJ+Kggw6KDRs2tHxOHdHZ89oTTjhhws9rc59LVZWDpe7T5/alSrSXc8Zy5aTlnHZap8+pJ3M/KKdNODLOod2zz/F1f7NnhK9+9asb2hDPfe5zx4QZf490fH0zfuzGxo0bG/ZTHyal1DTfXH755fGtb32rbVl56aWXVpanpdJpibGfEeX66hPtk+2www4dxyenPP3a177WcC122223MWFyysGcez0528kZQ5SznVLtg/p8U6vVmrbTcursH/zgBxERle3GqrZl/WpercYZrV69OjZt2hSf//znW46xyhmvVKLeyjnHpfrzOfk4R86+So1xKPWsKKe8bDaubvzKJDnP7S655JK48sor29aj9flm3333jX/7t39r2M7y5cvH1EnNVrnISac5ctN7qXsV7czJSRhvfvOb49xzz43Vq1fHEUccEc997nPj/PPPHxNmw4YNsXLlyrj11ltj2bJlDcunbLHFFmM698cdd1zDfnLCRET85Cc/ifPPPz8+9KEPxec///k46qijJnRc2267bXz0ox+NRYsWxSGHHBK33XZbQ5jh4eF49NFH45Of/GRERNx5551jfn/88cdj+fLlsd9++8VVV10Vf/VXf9WwjR/96Efxwx/+MFauXBkR0XTCyB//8R/H85///HjXu94VRx99dNNlZXL2lWPDhg2xatWqltcqIuK///u/Y8OGDfHII4/EM5/5zHjmM5/ZEGaLLbaIY489dvRzs+uVk3Zy9pXj4YcfjuXLl8emTZvizjvvjF/84hcNYXKueU6Yn//8523Te0R12hk57l/84hdtjzsnX7ziFa+I3XffPSKeLiTvuOOOKQvzyle+crShOZntjM/H9QM0IvLTe9V2FixYEBdffPHo52YdsZy0k3MdcuJcqizsZpic83PLLbfEmWeeGS9+8YvjpS99aSxZsmRC2ylVzlXlv4i8Y8/J61Vlas42cs7fPffcE1tvvXU873nPi4iIZz3rWRMKUyot59Rb22yzTfzsZz+Liy66KJ7znOfE448/3hAmJ4/mlMt9fX3xF3/xF/EXf/EX8eCDD8by5csbburlHNcf/dEfVR7XRMq4Zm2VHXfcMbbddtt4//vfH1tuuWUsWLCgIUxOGyInzqWue1WZG/Gb8zOS1pudn5xjzwlTlQ5ztpFz7KXi+/3vfz+23nrr2H777SOieR6NqC7DRvJ6u+3k5Juc9JWTlnPahDnth5x6YnyYRx55pCFMTtsy59hz8k1OfZNTD+Rcr5xrkVM+5eT17bbbLrbddts4++yz46STTmp6XDnld1VfIGc/OeevVH8i59zkxCenD5RTVpboc+TGp1TfLict52wnJ0ypdmPOec6Rez+jSlX5lNNuLHX+ctJXRN45zAlTVVfklHEl9pO7r6r7YLlhcq5pqXI5Zzs56SenL5ATn1JlWIn7OLllStVx5eabnHA57fxS9xPHlz3j2/k5badSfZecY8o5fzl9l1J1Uk6cu9kny+mL54SZSH+hXR8xonmZmlMORuSVqVVpI+feQU6YUvePS/UDctJpN+8ddHI/u117uJv3sHK2U6q/kFOGlcp/Vek5px/ezfOTky5ywuT0EXO2k1MelKpvcvJ6qXSRkwZL3d/LuV+dk29KlSs59V8nz9Jy0mqr7ZR6PhFRnTZK1Vs5eaLUM/GcfeWUYVXt3Ihyz9JyznNO2snZTqlnezn7yulHlmp/lthXqXzTyb3YdtczJ0/knJuI6vokJ2/ltAlzwuTUtTn1X06dVKr/nHMtSrX3Sj2PzLmmOc+LquKT2yfr1vPanPZMTjlY6j59zrUq1V7OGcuVk5ZLPdcsdT+ok3EO7dJyzjPCnDRYVd/k5ImcMDllZam+S046LTH2M6JcX71UnywnPjnlac61KDXmMCdMThrL2U6p9kFOOy2nzi7Vbsx5/pBT9uRsp0S9lXOOS/Xnc/Jxjpx9TeTe5kSPq1S9npMucvJfTj2ak29KPf/LkbOdkvcq2pmTkzAinq7YFi1aFIsWLWo642bevHkREfG6170uUkqxePHiMQXG7/3e78W73/3u2G677eLhhx+OV77ylQ3byAkTEfHoo4/GZz/72XjGM54xGreJmD9/fhxyyCHx8pe/PJYuXdo0Qey9995x4YUXxu677x7Pf/7zG2bEDQwMRETE6aef3nI/d99995jK+YknnmgIc8IJJ8Qdd9wRH/jAB+LRRx9tup2cfeWoulYREcccc0ycfvrpo7Od6mcQjsi9XlVpJ2dfOc4999y49957Y9WqVfGVr3wlTj755IYwOdc8J0zOOaxKO7nHnXOeDzjggDj11FNHP//N3/zNlIXZf//9i2xnfD7ecsstx/yem96rtnPSSSeN+Tx+1n1EXtrJuQ45cS5VFnYzTM75WbBgQZxyyinx7W9/O7bZZpumx56znVLlXFX+i8g79py8XhUmZxs55+/Tn/706LYiIvbZZ58JhSmVlnPqrc985jNx9dVXx7333htbb7110zfJ5uTRnHL5sMMOG/17xx13jBNPPHHKjmsiZVyztsqhhx465v9vetObGsKUakOUuu5VZW5E3vnJOfacMFXHnrONiOpjLxXfnDwaUV2G5WwnJ9/kpK+ctJyTvnLaDzn1RE6YnDbW2rVrY+utt2577DnHlVPf5NQDOdcr51rk5L+cvP62t71t9O/TTjstXvCCFzSEySm/R+LZqi+Qs5/68xfx9DFOpO+SI+fc5FzPiOo+UM61yjn2nDSYE59SfbuctJyznZww3ewf58g9h1WqyqecdmNEmfOXm75KtJkjquuKTvPNRPdTcl+l+gKlyuWc7URUp5+c9mdOfEqVYSXu4+SWKVXHlXtMOeFyznOp+4nj24W1Wq3t783aTqX6Ljlhcs5fTt8lokyd1Gt9spy+eE6YnDZziT5ibt1W4n5szr2DnDCdljsRzdtypfoBI9/3SjotdT+7m/ewcrZTqr+QU4aVyn9V6Tmnjx3RvfNT6hlGTh8xZzs55UGp+iYnr5dKFzlpsNT9vZx7WDnlZalyJaf+69bztlLPJyKq22Gl6q2cPJGTTnPK5Zx95ZRh4+Mzvp0bUe5ZWs55zkk7Odsp9WwvZ185/chS7c8S+8pJyxHV+abUvdicPFF/TM94xjParvTarj4p1d/KCZN7P7aq/supk0r1n3P6rJ22ZyKap7FSbdScMDl1bVV8cvtk3Xpem9OeKXU/OycfT1d/YiR+49NXTlou9VwzpwzLCVNqnENO3ZaTBqvqm5w8kRMmp6ws1XfJSacRnY39HNn3RNrmOem9VJ8sJz6l6rZSYw5zwuSksZztlKpvctppOXV2p+3GVm2jnOcPOeVKznZK1Fs557hUfz63PVwlZ18Tubc50eOKKFOv56SLnPyXU4/m5JuS4zKq5Gyn5L2KttIcdOONN6alS5emtWvXppRS+tKXvtQQ5uqrrx4T5tRTT23YxhFHHJG+853vpE2bNjXdRk6YlFJ64okn0t133z36+eyzz57gkf3Gww8/nM4666ymvz355JNp/fr1adOmTQ37Wr16dVq2bFm65ZZb0v77758uu+yyyn212k9KTx/bLbfcknVMn/nMZyrDNPPYY49lx6fKpk2bWp6bZkpcq1LaXfOqMLnnsF3a6cRUnOfpDlOVj8eXO5/97GebbnsqyoNWqq5DbnmQcz17LUy3TKRMbaUq/61evTodd9xx6eabb06LFi1KX//61xvCVNVtOWFyttFNOXVtznX45je/mZYtW5Zuvvnm9MY3vrHp+ZsKOWV3K6WOa6R8WrduXUqpebtoqsqmibYhcttY9Zq1M3KOK+f8lNZJ+ynHRNtYJU22DXHjjTem973vfWnt2rXp4YcfThdeeGHlv2mWvnKveafpa7rrm/rzk1JKZ5xxRtNwOXVk1bXqtO3dqpybqvzXzfQ+ket+9dVXjym7p7K+yaknJtKXqmo3HnDAAS3bIVXHfuONN6Yjjzwy3X777W372VX9+Zx2Uc52csqe3O10GqZVf6FKybZaiXZ1bvlUtY0S5y8nfaWUly9K5Z2q36diP5PZV6/1BXLkpJ9SfYHcNFav6nq1qkerrkWpvmgnx5Tb3stp65a+n1iVd3Ly1mTvf1b1b6rO8z/+4z+mvfbaa/Saf/GLX6yM80TLg5tuuiktXbo03X777emWW26ZcF2bkw67eR8sp07qZh+x2/dj28lp8+S25aq2Uyot55TdpdJpp9tp1R6eSBu1WZsmJ4/mto2qwuTU/blt+Kr8N5G0UUpOXdIsTFU+zk0XVWFylNpOqTyaW56WSBc5abDU+cmRW17mPGMuUd/kbKdEmJxjmmh/YnzeKlVv5chJp918tpATn9x6K6dOqjrPOXkrdztV8ckxkXvIvW6iaXl8vskpTzu9nilN7D5PK+Pripw2T6kwJeI7Iqf9XrL/PCKnX9usPZNbb5Voo5a6FqXuo9abzPPa8caf59z8V+p+dlWdntvn7/S+0kTT13ilrkMzOWXYVJZzPC2nvulkjEPV2M+SbfOcPnaJ9ntVfEqWp520nSYzLrFTVdspfY+5lW7cl+u2qvScU2/lpMFS/fmSfbtO7l9N5t5myefd3bqXMVtN5b2KenNyJYzzzjsvvvzlL8dpp50Wv/zlL2PdunUNYZYvXz4mzPjZRvXb2LhxY9Nt5ISJGDvbMKUUd9111+hMxE4ccsghDdv5yEc+Uhmmfl+XXHJJLF++PPbee++4/fbb46ijjop3vvOdxffTKszHP/7xTg45IsYutdoqPjkmGueJXKtSJnotxofJOYeljr3Uee61MFX5eHy58+Mf/ziaKVUeVMk5pomWB71+rbop5xzmmOj12m+//caEqarbcsLkbKObcuranOtw8cUXV56/UnLK5Ryljqv+HD7yyCNNz2EvtVXGx7nVdc9pZ+QcV875KWUq69qJtLFKKXFc9ddh3333jX/7t3/L2s/49NXpNe8kfU1nfTO+nXH//fc3hClVj0603TjZa9Eq/3UzvZe47l/+8pe7Vt/k1BOl+gE57ZCcY6+/5rVarbKf3ao/nxOfnO3klD2dbic3TKv+QpVSbbVS5VxO+dTpNiZ6/nLSV0ReviiRd6ayzJ2q8r3X+gI5ctJPqb5AThqb6PXq9FqU6ovm5pvp7vePr/urzmGp+2mljinnPF988cVx5ZVXjl7z733vew1hSsXn3HPPHVP/TbSuzUmH3boPFpFXJ3Wzj9jN+7FVcto8nbblWm2nVFrOKbtLpdOJbmciYXLaNDl5tNO2UaswOXX/RNrwzfJfTtooZaJ1yfgwVfm4VLrIUWo7pfJop+XpZNJFThosdX5ydFpe5jxjnkx9k7OdEmFyjim3P1HVDitVb+XITafdSl858ZmKOqnVee7mdjo9P632NdPkpuWqfJNTnk6krTKR+zzt4lxfV+S0eUqFKRHfXgiT058Y357ptN6aTBu11LUodR+11PPaqvM8VflvonX6RPv8OeNEJpK+Sl2HHDllWMlyjuZy0nunYxxa5YmpaJvn9LFLtd9bxWcqytPJPH+fyvJ0qu7HTjQ+0zkeoJSq9JxTb+WkwVL9+ano2+Xcv5rMvc1uPu+mvZL3KtqZk5Mwtttuu9h2223j7LPPjpNOOinuvPPOjsOU2MaIBQsWxMUXXzz6+eijj57QceVspyrMjjvuGNtuu228//3vjy233DIWLFgwJfvJDZOjl85ft3XzWvTaeZ5pYbpdHlTJ2U+vlQczMY/mnMMcpa5Xt+q/bsqJT865KXWtcpRKp6WOK+cc9lodUCrOpfZVSq+1jUopEZ9eu+a9do67eX66GabXrkWJfXWzvulmW66bdVKpur/UvroZJkep7ZTKWyXi0+1z3K1yrtfK3F5rF5VSqswota9uXYtuHlPJ45qr+a9UGdbN+JSq+7vZlit1DrtZ13br/PRa+6qb7c9eC9Nr7c9eC1NKt8r4bt6TnI3tmZJheq0v3mt9xJn23LzX2vjdTF/dbKd1s33Q7f7LTJJ7brqVb0qe4xJx7mZ52mv9yG7e1+21a9Fr9z97Kf91s+3UzfEmpfRav2SumoltlV4r47q5r9k6nrBUfGaibvXteq0/32vH1Wt9zdmqa2Vq+4UyZqfVq1eP+XzBBRd0HKbENkb853/+55jPv/jFL5qGq5Kznaowt91225jP3/jGN6ZkP7lhcvTS+eu2bl6LXjvPMy1Mt8uDKjn76bXyYCbm0ZxzmKPU9epW/ddNOfHJOTelrlWOUum01HHlnMNeqwNKxbnUvkrptbZRKSXi02vXvNfOcTfPTzfD9Nq1KLGvbtY33WzLdbNOKlX3l9pXN8PkKLWdUnmrRHy6fY67Vc71Wpnba+2iUkqVGaX21a1r0c1jSqn30upMy3+lyrBuxqdU3d/Ntlypc9jNurZb56fX2lfdbH/2Wphea3/2WphSulXGd/Oe5Gxsz5QM02t98V7rI8605+a91sbvZvrqZjutm+2DbvdfZpLcc9OtfFPyHJeIczfL017rR3bzvm6vXYteu//ZS/mvm22nbo43KaXX+iVz1Uxsq/RaGdfNfc3W8YSl4jMTdatv12v9+V47rl7ra85W3SpT56WUUoFJIwAAAAAAAAAAAAAAALPaFtMdAQAAAAAAAAAAAAAAgJnAJAwAAAAAAAAAAAAAAIAMJmEAAAAAAAAAAAAAAABkMAkDAAAAAAAAAAAAAAAgg0kYAAAAAAAAAAAAAAAAGUzCAAAAAAAAAAAAAAAAyGASBgAAAAAAAAAAAAAAQAaTMAAAAAAAAAAAAAAAADL8/8pAqA9Cx1+5AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 4000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = linkage(x, method=\"ward\")\n",
    "plt.figure(figsize=(40, 15))\n",
    "dg = dendrogram(Z)\n",
    "plt.title(\"Agglomerative Clustering\")\n",
    "ax = plt.gca()\n",
    "\n",
    "bounds = ax.get_xbound()\n",
    "\n",
    "ax.plot(bounds, [0.09, 0.09], '--', c='r')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "de6e4bf4",
   "metadata": {},
   "outputs": [
    {
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       "        0, 20,  0,  0,  6,  6], dtype=int64)"
      ]
     },
     "execution_count": 13,
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   ],
   "source": [
    "agg = AgglomerativeClustering(n_clusters=26,linkage=\"ward\")\n",
    "agg = agg.fit(x)\n",
    "re=agg.labels_\n",
    "re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "98df3bac",
   "metadata": {},
   "outputs": [
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     "name": "stderr",
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      "c:\\ProgramData\\Anaconda3\\envs\\py37\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
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      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
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   "id": "de78b83d",
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   "outputs": [],
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       "..                                                 ...  ..\n",
       "330                 운영체제는 시스템의 행동을계산하고 알려주는 도구를 제공해야한다   0\n",
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       "                        Document  re\n",
       "157                     상태 정보 전달   2\n",
       "195                        상태 정보   2\n",
       "204   상태 정보 (Status Information)   2\n",
       "283                     효율성이 낮다    2"
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     "execution_count": 19,
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       "                                              Document  re\n",
       "130       프로세스 속성(attributes) 획득(get), 프로세스 속성 설정(set)   3\n",
       "142        파일 속성 획득 및 설정 (get and set file attributes)   3\n",
       "143                          장치 관리 (Device management)   3\n",
       "144      장치 요청, 장치 방출 (request device, release device)   3\n",
       "146  장치 속성 획득 및 설정 (get device attributes, set devi...   3\n",
       "150  시간과 날짜의 설정과 획득(get time or date, set time or d...   3\n",
       "151  시스템 데이터의 설정과 획득(get system data, set system data)   3\n",
       "152  프로세스, 파일, 장치 속성의 획득과 설정(get and set process, f...   3\n",
       "169                                            C 라이브러리   3\n",
       "171                            단일 태스킹(monoprogramming)   3\n",
       "179                           다중 태스킹(multiprogramming)   3\n",
       "269                                           최초의 UNIX   3\n",
       "294                                                 단점   3"
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       "                                              Document  re\n",
       "15          운영체제는 사용자(또는 프로그램) 들에게 프로그램의 실행 환경을 제공한다    10\n",
       "24                                 프로그램 실행 (Execution)  10\n",
       "25                프로그램을 메모리에 적재, 실행, 실행 종료 (정상 또는 비정상)  10\n",
       "129                              적재(load), 실행(execute)  10\n",
       "135                           버그 찾는 debugger, 한 단계씩 실행  10\n",
       "173                            하나의 프로그램을 실행하는간단한 방법 제공  10\n",
       "182                        Exec()을 실행하여 프로그램을 프로세스에적재함  10\n",
       "198                                        프로그램 적재와 실행  10\n",
       "215                                 프로그램 적재와 실행 (p ~ )  10\n",
       "325   실행환경이 핵심 라이브러리 집합과 Dalvik 가상 머신 포함Application...  10"
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   "outputs": [
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       "                                     Document  re\n",
       "30                                     파일 시스템  15\n",
       "31                            파일과 디렉토리 생성과 삭제  15\n",
       "32                             파일과 디렉토리 읽고 쓰기  15\n",
       "33                                파일과 디렉토리 찾기  15\n",
       "34                                   파일 정보 보기  15\n",
       "138                   파일 관리 (File management)  15\n",
       "139  파일 생성 (create file), 파일 삭제 (delete file)  15\n",
       "140          파일 열기 (open), 파일 닫기 (close file)  15\n",
       "149            정보 관리(Information maintenance)  15\n",
       "194                                     파일 관리  15\n",
       "202                  파일 관리 (File Management)   15\n",
       "210                                     파일 변경  15"
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   "id": "fc934850",
   "metadata": {},
   "outputs": [
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       "      <td>20</td>\n",
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       "      <td>20</td>\n",
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       "      <td>20</td>\n",
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       "    <tr>\n",
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       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>사용자 업무를 수행하는 데 필요한 기능을 제공하는 운영체제 서비스</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>사용자 업무를 수행하는 데 필요한 기능을 제공하는 운영체제 서비스</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>운영체제는 가능한 모든 오류를 인지하고 있어야 함</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>107</th>\n",
       "      <td>운영체제 인터페이스의 자세한내용은 API에 의해 숨겨진다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>113</th>\n",
       "      <td>운영체제에 매개변수를 전달하는 세가지 일반적인 방법  가장 간단한 방법: 매개변수...</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>114</th>\n",
       "      <td>문제는 레지스터 숫자보다 더 많은 매개변수가 있는 경우는레지스터만으로는 부족하다 ...</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>116</th>\n",
       "      <td>운영체제에 매개변수를 전달하는 세가지 일반적인 방법   매개변수가 메모리에 있는 ...</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>117</th>\n",
       "      <td>블록의 메모리 주소가 레지스터에 매개변수로 전달된다</td>\n",
       "      <td>20</td>\n",
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       "    <tr>\n",
       "      <th>120</th>\n",
       "      <td>운영체제에 매개변수를 전달하는 세가지 일반적인 방법</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>프로그램이 매개변수를 스택에 넣는다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>운영체제가 스택으로부터 매개변수를 꺼낸다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>블록과 스택 방법은 매개변수 개수나 길이에 제약을 가하지 않는다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>177</th>\n",
       "      <td>프로그램 종료 -&gt; 쉘을 재적재At system startup          r...</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>183</th>\n",
       "      <td>쉘은 프로세스 종료를 기다리거나 또는 다른사용자 명령을 수행한다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>226</th>\n",
       "      <td>사용자 모드에서 수행</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>228</th>\n",
       "      <td>운영체제 설계 및 구현</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>229</th>\n",
       "      <td>운영체제의 설계와 구현에 완전한 해결책은 없지만, 성공적인 접근 방법들이존재한다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>230</th>\n",
       "      <td>운영체제 별로 내부 구조는 매우 다르다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>236</th>\n",
       "      <td>운영체제 설계 및 구현 : 정책과 기법</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>257</th>\n",
       "      <td>Language)로 된 운영체제</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>260</th>\n",
       "      <td>Emulation으로 운영체제를 다른 하드웨어에서 수행되는 것도 가능하다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>262</th>\n",
       "      <td>범용 운영체제는 매우 큰 프로그램이다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>263</th>\n",
       "      <td>다양한 방법으로 운영체제의 구성을 할 수 있다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>270</th>\n",
       "      <td>하드웨어 기능의 제약</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>277</th>\n",
       "      <td>운영체제는 여러 개의 계층(layer, level)으로 구분</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>334</th>\n",
       "      <td>운영체제는 같은 종류의 어떤 기계에서도 수행되도록 설계된다; 운영체제는특정한 컴퓨...</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>341</th>\n",
       "      <td>운영체제를 하드웨어가 시작할 수 있도록 하드웨어에게 알려야 한다</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                              Document  re\n",
       "1     운영체제 사용자 인터페이스 (User Operating System Interface)  20\n",
       "5     운영체제 설계 및 구현 (Operating System Design and Imp...  20\n",
       "7                운영체제 디버깅 (Operating System Debugging)  20\n",
       "12                            운영체제를 구성하는 다양한 방법에 대해 논의  20\n",
       "13             운영체제 설치, 맞춤화 과정(customization), 부팅하는 방법  20\n",
       "18                사용자 업무를 수행하는 데 필요한 기능을 제공하는 운영체제 서비스  20\n",
       "23                사용자 업무를 수행하는 데 필요한 기능을 제공하는 운영체제 서비스  20\n",
       "29                사용자 업무를 수행하는 데 필요한 기능을 제공하는 운영체제 서비스  20\n",
       "40                사용자 업무를 수행하는 데 필요한 기능을 제공하는 운영체제 서비스  20\n",
       "42                         운영체제는 가능한 모든 오류를 인지하고 있어야 함  20\n",
       "107                    운영체제 인터페이스의 자세한내용은 API에 의해 숨겨진다  20\n",
       "113   운영체제에 매개변수를 전달하는 세가지 일반적인 방법  가장 간단한 방법: 매개변수...  20\n",
       "114   문제는 레지스터 숫자보다 더 많은 매개변수가 있는 경우는레지스터만으로는 부족하다 ...  20\n",
       "116   운영체제에 매개변수를 전달하는 세가지 일반적인 방법   매개변수가 메모리에 있는 ...  20\n",
       "117                      블록의 메모리 주소가 레지스터에 매개변수로 전달된다   20\n",
       "120                      운영체제에 매개변수를 전달하는 세가지 일반적인 방법   20\n",
       "121                                프로그램이 매개변수를 스택에 넣는다  20\n",
       "122                             운영체제가 스택으로부터 매개변수를 꺼낸다  20\n",
       "123               블록과 스택 방법은 매개변수 개수나 길이에 제약을 가하지 않는다   20\n",
       "177   프로그램 종료 -> 쉘을 재적재At system startup          r...  20\n",
       "183                쉘은 프로세스 종료를 기다리거나 또는 다른사용자 명령을 수행한다  20\n",
       "226                                        사용자 모드에서 수행  20\n",
       "228                                       운영체제 설계 및 구현  20\n",
       "229       운영체제의 설계와 구현에 완전한 해결책은 없지만, 성공적인 접근 방법들이존재한다  20\n",
       "230                              운영체제 별로 내부 구조는 매우 다르다  20\n",
       "236                              운영체제 설계 및 구현 : 정책과 기법  20\n",
       "257                                  Language)로 된 운영체제  20\n",
       "260          Emulation으로 운영체제를 다른 하드웨어에서 수행되는 것도 가능하다   20\n",
       "262                               범용 운영체제는 매우 큰 프로그램이다  20\n",
       "263                          다양한 방법으로 운영체제의 구성을 할 수 있다  20\n",
       "270                                        하드웨어 기능의 제약  20\n",
       "277                  운영체제는 여러 개의 계층(layer, level)으로 구분  20\n",
       "334   운영체제는 같은 종류의 어떤 기계에서도 수행되도록 설계된다; 운영체제는특정한 컴퓨...  20\n",
       "341                운영체제를 하드웨어가 시작할 수 있도록 하드웨어에게 알려야 한다  20"
      ]
     },
     "execution_count": 23,
     "metadata": {},
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   "source": [
    "y[y['re']==20]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "350448b3",
   "metadata": {},
   "outputs": [
    {
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   "cell_type": "code",
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",
      "text/plain": [
       "<Figure size 4000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = linkage(x, method=\"single\")\n",
    "plt.figure(figsize=(40, 15))\n",
    "dg = dendrogram(Z)\n",
    "plt.title(\"Agglomerative Clustering\")\n",
    "ax = plt.gca()\n",
    "\n",
    "bounds = ax.get_xbound()\n",
    "\n",
    "ax.plot(bounds, [0.09, 0.09], '--', c='k')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "cfa7090c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = linkage(x, method=\"complete\")\n",
    "plt.figure(figsize=(40, 15))\n",
    "dg = dendrogram(Z)\n",
    "plt.title(\"Agglomerative Clustering\")\n",
    "ax = plt.gca()\n",
    "\n",
    "bounds = ax.get_xbound()\n",
    "\n",
    "ax.plot(bounds, [0.09, 0.09], '--', c='k')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b52895ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = linkage(x, method=\"average\")\n",
    "plt.figure(figsize=(40, 15))\n",
    "dg = dendrogram(Z)\n",
    "plt.title(\"Agglomerative Clustering\")\n",
    "ax = plt.gca()\n",
    "\n",
    "bounds = ax.get_xbound()\n",
    "\n",
    "ax.plot(bounds, [0.09, 0.09], '--', c='k')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "65335f05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = linkage(x, method=\"weighted\")\n",
    "plt.figure(figsize=(40, 15))\n",
    "dg = dendrogram(Z)\n",
    "plt.title(\"Agglomerative Clustering\")\n",
    "ax = plt.gca()\n",
    "\n",
    "bounds = ax.get_xbound()\n",
    "\n",
    "ax.plot(bounds, [0.09, 0.09], '--', c='k')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "8401feba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 4000x1500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Z = linkage(x, method=\"median\")\n",
    "plt.figure(figsize=(40, 15))\n",
    "dg = dendrogram(Z)\n",
    "plt.title(\"Agglomerative Clustering\")\n",
    "ax = plt.gca()\n",
    "\n",
    "bounds = ax.get_xbound()\n",
    "\n",
    "ax.plot(bounds, [0.09, 0.09], '--', c='k')\n",
    "\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "py37",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  },
  "vscode": {
   "interpreter": {
    "hash": "b79a61544c9a744d09395b396d14bdc3ab2980641b64ddb1c7bc6d7b892900a0"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}