DBSCANClustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://hoonzi-text.tistory.com/19\n",
    "# https://techblog-history-younghunjo1.tistory.com/114\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from konlpy.tag import Okt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data size-> 346\n"
     ]
    }
   ],
   "source": [
    "# 데이터 가져오기\n",
    "df = pd.read_csv('./final.csv')\n",
    "print('data size->', len(df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Document</th>\n",
       "      <th>tokenizedDocument</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>...</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</td>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>운영체제 서비스</td>\n",
       "      <td>0.714455</td>\n",
       "      <td>-0.531385</td>\n",
       "      <td>2.488015</td>\n",
       "      <td>-0.770630</td>\n",
       "      <td>0.112286</td>\n",
       "      <td>0.774671</td>\n",
       "      <td>2.070887</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.610353</td>\n",
       "      <td>-1.074397</td>\n",
       "      <td>-0.708204</td>\n",
       "      <td>-0.606940</td>\n",
       "      <td>-1.245999</td>\n",
       "      <td>1.037042</td>\n",
       "      <td>0.395951</td>\n",
       "      <td>0.475926</td>\n",
       "      <td>0.371759</td>\n",
       "      <td>-1.622231</td>\n",
       "    </tr>\n",
       "    <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>1.373194</td>\n",
       "      <td>-2.268541</td>\n",
       "      <td>2.041227</td>\n",
       "      <td>-1.548548</td>\n",
       "      <td>0.202811</td>\n",
       "      <td>0.251074</td>\n",
       "      <td>0.367272</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.636907</td>\n",
       "      <td>-0.851541</td>\n",
       "      <td>-2.177040</td>\n",
       "      <td>-1.377887</td>\n",
       "      <td>0.282307</td>\n",
       "      <td>1.705836</td>\n",
       "      <td>1.317941</td>\n",
       "      <td>1.302224</td>\n",
       "      <td>-1.182694</td>\n",
       "      <td>-0.189527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>시스템 콜 (System Calls)</td>\n",
       "      <td>시스템 콜</td>\n",
       "      <td>0.011394</td>\n",
       "      <td>-0.826875</td>\n",
       "      <td>2.973761</td>\n",
       "      <td>-0.366313</td>\n",
       "      <td>-0.839046</td>\n",
       "      <td>0.789231</td>\n",
       "      <td>0.961213</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.595384</td>\n",
       "      <td>0.178099</td>\n",
       "      <td>1.141711</td>\n",
       "      <td>-1.204290</td>\n",
       "      <td>-0.019782</td>\n",
       "      <td>0.544513</td>\n",
       "      <td>2.611695</td>\n",
       "      <td>1.183234</td>\n",
       "      <td>-0.109376</td>\n",
       "      <td>-0.170399</td>\n",
       "    </tr>\n",
       "    <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>-0.314897</td>\n",
       "      <td>-1.808564</td>\n",
       "      <td>2.901775</td>\n",
       "      <td>-0.102677</td>\n",
       "      <td>-0.380242</td>\n",
       "      <td>0.683322</td>\n",
       "      <td>0.860649</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153475</td>\n",
       "      <td>0.073037</td>\n",
       "      <td>1.136598</td>\n",
       "      <td>-1.001038</td>\n",
       "      <td>0.101517</td>\n",
       "      <td>0.996975</td>\n",
       "      <td>2.293804</td>\n",
       "      <td>1.101085</td>\n",
       "      <td>-0.827427</td>\n",
       "      <td>0.046568</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>0.597797</td>\n",
       "      <td>-4.119634</td>\n",
       "      <td>4.071386</td>\n",
       "      <td>-0.778798</td>\n",
       "      <td>-0.830832</td>\n",
       "      <td>0.947624</td>\n",
       "      <td>2.868076</td>\n",
       "      <td>...</td>\n",
       "      <td>1.820633</td>\n",
       "      <td>-0.196410</td>\n",
       "      <td>-0.282522</td>\n",
       "      <td>-2.117036</td>\n",
       "      <td>1.165344</td>\n",
       "      <td>1.074218</td>\n",
       "      <td>3.273731</td>\n",
       "      <td>1.062482</td>\n",
       "      <td>0.584410</td>\n",
       "      <td>-1.640707</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 103 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "  tokenizedDocument       dm1       dm2       dm3       dm4       dm5  \\\n",
       "0          운영체제 서비스  0.714455 -0.531385  2.488015 -0.770630  0.112286   \n",
       "1    운영체제 사용자 인터페이스  1.373194 -2.268541  2.041227 -1.548548  0.202811   \n",
       "2             시스템 콜  0.011394 -0.826875  2.973761 -0.366313 -0.839046   \n",
       "3          시스템 콜 유형 -0.314897 -1.808564  2.901775 -0.102677 -0.380242   \n",
       "4          시스템 프로그램  0.597797 -4.119634  4.071386 -0.778798 -0.830832   \n",
       "\n",
       "        dm6       dm7  ...      dm91      dm92      dm93      dm94      dm95  \\\n",
       "0  0.774671  2.070887  ... -0.610353 -1.074397 -0.708204 -0.606940 -1.245999   \n",
       "1  0.251074  0.367272  ... -0.636907 -0.851541 -2.177040 -1.377887  0.282307   \n",
       "2  0.789231  0.961213  ... -0.595384  0.178099  1.141711 -1.204290 -0.019782   \n",
       "3  0.683322  0.860649  ...  0.153475  0.073037  1.136598 -1.001038  0.101517   \n",
       "4  0.947624  2.868076  ...  1.820633 -0.196410 -0.282522 -2.117036  1.165344   \n",
       "\n",
       "       dm96      dm97      dm98      dm99     dm100  \n",
       "0  1.037042  0.395951  0.475926  0.371759 -1.622231  \n",
       "1  1.705836  1.317941  1.302224 -1.182694 -0.189527  \n",
       "2  0.544513  2.611695  1.183234 -0.109376 -0.170399  \n",
       "3  0.996975  2.293804  1.101085 -0.827427  0.046568  \n",
       "4  1.074218  3.273731  1.062482  0.584410 -1.640707  \n",
       "\n",
       "[5 rows x 103 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()\n",
    "# df[:5] # 상위 5개 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "# NULL 값 존재 유무\n",
    "print(df.isnull().values.any())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "False\n"
     ]
    }
   ],
   "source": [
    "df = df.dropna(how = 'any') # Null 값이 존재하는 행 제거\n",
    "print(df.isnull().values.any()) # Null 값이 존재하는지 확인"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "346\n"
     ]
    }
   ],
   "source": [
    "print(len(df)) # 개수 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "okt = Okt() # 형태소 분석기 객체 생성\n",
    "\n",
    "'''\n",
    "# Okt의 nouns를 사용한 토큰화\n",
    "noun_list = []\n",
    "for content in df['tokenizedDocument']: \n",
    "    nouns = okt.nouns(content) # 명사만 추출하기, 결과값은 명사 리스트\n",
    "    noun_list.append(nouns)\n",
    "'''\n",
    "\n",
    "# Okt의 morph를 사용한 토큰화\n",
    "tokenized_data = []\n",
    "for sentence in df['tokenizedDocument']:\n",
    "    tokenized_sentence = okt.morphs(sentence, stem=True) # 토큰화\n",
    "    tokenized_data.append(tokenized_sentence)\n",
    "\n",
    "# 명사 추출 방식의 토큰화에 사용\n",
    "# df['nouns'] = noun_list\n",
    "# tokenized_data = df['nouns'].tolist()\n",
    "\n",
    "# morph 방식의 토큰화에 사용\n",
    "df['tokenized_data'] = tokenized_data\n",
    "tokenized_data = df['tokenized_data'].tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "최대 길이 : 34\n",
      "평균 길이 : 5.07514450867052\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 길이 분포 확인\n",
    "print('최대 길이 :',max(len(review) for review in tokenized_data))\n",
    "print('평균 길이 :',sum(map(len, tokenized_data))/len(tokenized_data))\n",
    "plt.hist([len(review) for review in tokenized_data], bins=50)\n",
    "plt.xlabel('length of samples')\n",
    "plt.ylabel('number of samples')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['운영체제', '서비스'],\n",
       " ['운영체제', '사용자', '인터페이스'],\n",
       " ['시스템', '콜'],\n",
       " ['시스템', '콜', '유형'],\n",
       " ['시스템', '프로그램']]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tokenized_data[:5] # 상위 5개 출력"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_temp = df.iloc[:346, 3:103] #346"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <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>0.714455</td>\n",
       "      <td>-0.531385</td>\n",
       "      <td>2.488015</td>\n",
       "      <td>-0.770630</td>\n",
       "      <td>0.112286</td>\n",
       "      <td>0.774671</td>\n",
       "      <td>2.070887</td>\n",
       "      <td>1.770209</td>\n",
       "      <td>-2.353306</td>\n",
       "      <td>1.783039</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.610353</td>\n",
       "      <td>-1.074397</td>\n",
       "      <td>-0.708204</td>\n",
       "      <td>-0.606940</td>\n",
       "      <td>-1.245999</td>\n",
       "      <td>1.037042</td>\n",
       "      <td>0.395951</td>\n",
       "      <td>0.475926</td>\n",
       "      <td>0.371759</td>\n",
       "      <td>-1.622231</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.373194</td>\n",
       "      <td>-2.268541</td>\n",
       "      <td>2.041227</td>\n",
       "      <td>-1.548548</td>\n",
       "      <td>0.202811</td>\n",
       "      <td>0.251074</td>\n",
       "      <td>0.367272</td>\n",
       "      <td>1.672797</td>\n",
       "      <td>-2.467688</td>\n",
       "      <td>1.628929</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.636907</td>\n",
       "      <td>-0.851541</td>\n",
       "      <td>-2.177040</td>\n",
       "      <td>-1.377887</td>\n",
       "      <td>0.282307</td>\n",
       "      <td>1.705836</td>\n",
       "      <td>1.317941</td>\n",
       "      <td>1.302224</td>\n",
       "      <td>-1.182694</td>\n",
       "      <td>-0.189527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.011394</td>\n",
       "      <td>-0.826875</td>\n",
       "      <td>2.973761</td>\n",
       "      <td>-0.366313</td>\n",
       "      <td>-0.839046</td>\n",
       "      <td>0.789231</td>\n",
       "      <td>0.961213</td>\n",
       "      <td>1.684802</td>\n",
       "      <td>-3.608681</td>\n",
       "      <td>0.754566</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.595384</td>\n",
       "      <td>0.178099</td>\n",
       "      <td>1.141711</td>\n",
       "      <td>-1.204290</td>\n",
       "      <td>-0.019782</td>\n",
       "      <td>0.544513</td>\n",
       "      <td>2.611695</td>\n",
       "      <td>1.183234</td>\n",
       "      <td>-0.109376</td>\n",
       "      <td>-0.170399</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.314897</td>\n",
       "      <td>-1.808564</td>\n",
       "      <td>2.901775</td>\n",
       "      <td>-0.102677</td>\n",
       "      <td>-0.380242</td>\n",
       "      <td>0.683322</td>\n",
       "      <td>0.860649</td>\n",
       "      <td>1.900799</td>\n",
       "      <td>-3.395546</td>\n",
       "      <td>0.877328</td>\n",
       "      <td>...</td>\n",
       "      <td>0.153475</td>\n",
       "      <td>0.073037</td>\n",
       "      <td>1.136598</td>\n",
       "      <td>-1.001038</td>\n",
       "      <td>0.101517</td>\n",
       "      <td>0.996975</td>\n",
       "      <td>2.293804</td>\n",
       "      <td>1.101085</td>\n",
       "      <td>-0.827427</td>\n",
       "      <td>0.046568</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.597797</td>\n",
       "      <td>-4.119634</td>\n",
       "      <td>4.071386</td>\n",
       "      <td>-0.778798</td>\n",
       "      <td>-0.830832</td>\n",
       "      <td>0.947624</td>\n",
       "      <td>2.868076</td>\n",
       "      <td>3.423932</td>\n",
       "      <td>-5.139442</td>\n",
       "      <td>0.825764</td>\n",
       "      <td>...</td>\n",
       "      <td>1.820633</td>\n",
       "      <td>-0.196410</td>\n",
       "      <td>-0.282522</td>\n",
       "      <td>-2.117036</td>\n",
       "      <td>1.165344</td>\n",
       "      <td>1.074218</td>\n",
       "      <td>3.273731</td>\n",
       "      <td>1.062482</td>\n",
       "      <td>0.584410</td>\n",
       "      <td>-1.640707</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>0.222149</td>\n",
       "      <td>-3.372326</td>\n",
       "      <td>3.010398</td>\n",
       "      <td>-1.379068</td>\n",
       "      <td>-1.122083</td>\n",
       "      <td>-1.308177</td>\n",
       "      <td>-0.303563</td>\n",
       "      <td>1.678100</td>\n",
       "      <td>-2.084328</td>\n",
       "      <td>0.348584</td>\n",
       "      <td>...</td>\n",
       "      <td>0.933681</td>\n",
       "      <td>1.303830</td>\n",
       "      <td>-1.493170</td>\n",
       "      <td>-2.017200</td>\n",
       "      <td>0.774388</td>\n",
       "      <td>0.170071</td>\n",
       "      <td>0.344846</td>\n",
       "      <td>2.664122</td>\n",
       "      <td>-0.876150</td>\n",
       "      <td>-2.341106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342</th>\n",
       "      <td>0.212965</td>\n",
       "      <td>-1.827281</td>\n",
       "      <td>3.537088</td>\n",
       "      <td>-1.536552</td>\n",
       "      <td>-0.451717</td>\n",
       "      <td>-1.553486</td>\n",
       "      <td>0.283955</td>\n",
       "      <td>1.621245</td>\n",
       "      <td>-2.918727</td>\n",
       "      <td>0.658755</td>\n",
       "      <td>...</td>\n",
       "      <td>1.060881</td>\n",
       "      <td>-0.256200</td>\n",
       "      <td>-2.471534</td>\n",
       "      <td>-1.501794</td>\n",
       "      <td>-0.710068</td>\n",
       "      <td>0.662465</td>\n",
       "      <td>1.444199</td>\n",
       "      <td>-0.119418</td>\n",
       "      <td>-0.345229</td>\n",
       "      <td>-0.273893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>343</th>\n",
       "      <td>0.028034</td>\n",
       "      <td>-1.855329</td>\n",
       "      <td>1.726132</td>\n",
       "      <td>-0.514380</td>\n",
       "      <td>0.055517</td>\n",
       "      <td>-0.897894</td>\n",
       "      <td>-0.397566</td>\n",
       "      <td>1.820389</td>\n",
       "      <td>-1.111108</td>\n",
       "      <td>1.315148</td>\n",
       "      <td>...</td>\n",
       "      <td>1.477395</td>\n",
       "      <td>-0.121360</td>\n",
       "      <td>-0.036039</td>\n",
       "      <td>-0.523707</td>\n",
       "      <td>-0.609615</td>\n",
       "      <td>0.579980</td>\n",
       "      <td>0.493013</td>\n",
       "      <td>0.585409</td>\n",
       "      <td>-0.329471</td>\n",
       "      <td>-0.318402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>344</th>\n",
       "      <td>0.375210</td>\n",
       "      <td>-1.566365</td>\n",
       "      <td>1.840266</td>\n",
       "      <td>0.172384</td>\n",
       "      <td>-0.653272</td>\n",
       "      <td>0.393887</td>\n",
       "      <td>0.515321</td>\n",
       "      <td>1.490309</td>\n",
       "      <td>-2.052484</td>\n",
       "      <td>1.067986</td>\n",
       "      <td>...</td>\n",
       "      <td>2.143717</td>\n",
       "      <td>-0.657411</td>\n",
       "      <td>-1.112525</td>\n",
       "      <td>0.238495</td>\n",
       "      <td>0.045189</td>\n",
       "      <td>0.705270</td>\n",
       "      <td>1.014119</td>\n",
       "      <td>-0.178653</td>\n",
       "      <td>0.173723</td>\n",
       "      <td>-0.018322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>345</th>\n",
       "      <td>0.374511</td>\n",
       "      <td>-1.914504</td>\n",
       "      <td>4.014822</td>\n",
       "      <td>-0.143854</td>\n",
       "      <td>-1.400076</td>\n",
       "      <td>-1.051929</td>\n",
       "      <td>0.365397</td>\n",
       "      <td>1.416397</td>\n",
       "      <td>-3.880454</td>\n",
       "      <td>-0.277071</td>\n",
       "      <td>...</td>\n",
       "      <td>2.002055</td>\n",
       "      <td>0.340193</td>\n",
       "      <td>-0.562765</td>\n",
       "      <td>-1.474958</td>\n",
       "      <td>-0.630949</td>\n",
       "      <td>0.036674</td>\n",
       "      <td>1.768401</td>\n",
       "      <td>0.246813</td>\n",
       "      <td>-0.369874</td>\n",
       "      <td>-1.727772</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    0.714455 -0.531385  2.488015 -0.770630  0.112286  0.774671  2.070887   \n",
       "1    1.373194 -2.268541  2.041227 -1.548548  0.202811  0.251074  0.367272   \n",
       "2    0.011394 -0.826875  2.973761 -0.366313 -0.839046  0.789231  0.961213   \n",
       "3   -0.314897 -1.808564  2.901775 -0.102677 -0.380242  0.683322  0.860649   \n",
       "4    0.597797 -4.119634  4.071386 -0.778798 -0.830832  0.947624  2.868076   \n",
       "..        ...       ...       ...       ...       ...       ...       ...   \n",
       "341  0.222149 -3.372326  3.010398 -1.379068 -1.122083 -1.308177 -0.303563   \n",
       "342  0.212965 -1.827281  3.537088 -1.536552 -0.451717 -1.553486  0.283955   \n",
       "343  0.028034 -1.855329  1.726132 -0.514380  0.055517 -0.897894 -0.397566   \n",
       "344  0.375210 -1.566365  1.840266  0.172384 -0.653272  0.393887  0.515321   \n",
       "345  0.374511 -1.914504  4.014822 -0.143854 -1.400076 -1.051929  0.365397   \n",
       "\n",
       "          dm8       dm9      dm10  ...      dm91      dm92      dm93  \\\n",
       "0    1.770209 -2.353306  1.783039  ... -0.610353 -1.074397 -0.708204   \n",
       "1    1.672797 -2.467688  1.628929  ... -0.636907 -0.851541 -2.177040   \n",
       "2    1.684802 -3.608681  0.754566  ... -0.595384  0.178099  1.141711   \n",
       "3    1.900799 -3.395546  0.877328  ...  0.153475  0.073037  1.136598   \n",
       "4    3.423932 -5.139442  0.825764  ...  1.820633 -0.196410 -0.282522   \n",
       "..        ...       ...       ...  ...       ...       ...       ...   \n",
       "341  1.678100 -2.084328  0.348584  ...  0.933681  1.303830 -1.493170   \n",
       "342  1.621245 -2.918727  0.658755  ...  1.060881 -0.256200 -2.471534   \n",
       "343  1.820389 -1.111108  1.315148  ...  1.477395 -0.121360 -0.036039   \n",
       "344  1.490309 -2.052484  1.067986  ...  2.143717 -0.657411 -1.112525   \n",
       "345  1.416397 -3.880454 -0.277071  ...  2.002055  0.340193 -0.562765   \n",
       "\n",
       "         dm94      dm95      dm96      dm97      dm98      dm99     dm100  \n",
       "0   -0.606940 -1.245999  1.037042  0.395951  0.475926  0.371759 -1.622231  \n",
       "1   -1.377887  0.282307  1.705836  1.317941  1.302224 -1.182694 -0.189527  \n",
       "2   -1.204290 -0.019782  0.544513  2.611695  1.183234 -0.109376 -0.170399  \n",
       "3   -1.001038  0.101517  0.996975  2.293804  1.101085 -0.827427  0.046568  \n",
       "4   -2.117036  1.165344  1.074218  3.273731  1.062482  0.584410 -1.640707  \n",
       "..        ...       ...       ...       ...       ...       ...       ...  \n",
       "341 -2.017200  0.774388  0.170071  0.344846  2.664122 -0.876150 -2.341106  \n",
       "342 -1.501794 -0.710068  0.662465  1.444199 -0.119418 -0.345229 -0.273893  \n",
       "343 -0.523707 -0.609615  0.579980  0.493013  0.585409 -0.329471 -0.318402  \n",
       "344  0.238495  0.045189  0.705270  1.014119 -0.178653  0.173723 -0.018322  \n",
       "345 -1.474958 -0.630949  0.036674  1.768401  0.246813 -0.369874 -1.727772  \n",
       "\n",
       "[346 rows x 100 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## DBSCAN\n",
    "\n",
    "sklearn.cluster.DBSCAN 이용. https://scikit-learn.org/stable/modules/generated/sklearn.cluster.DBSCAN.html\n",
    "\n",
    "주요 파라미터:\n",
    "- `eps`: 서로 이웃으로 간주되는 두 샘플 사이의 최대 거리. 기본값=0.5.\n",
    "- `min_samples`: 어떤 샘플이 핵심 샘플로 간주되기 위해 자신의 이웃에 존재해야 하는 샘플의 개수(자신도 포함). 기본값=5.\n",
    "\n",
    "주요 속성:\n",
    "- `labels_`: 각 샘플의 클러스터 레이블(핵심 샘플의 인덱스). 이상치 샘플의 레이블은 -1.\n",
    "- `core_sample_indices_`: 핵심 샘플의 인덱스. (핵심 샘플 개수,) 모양의 다차원 배열.\n",
    "- `components_`: 핵심 샘플. (핵심 샘플 개수, 특성 개수) 모양의 다차원 배열."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# DBSCAN을 활용한 군집화\n",
    "from sklearn.cluster import DBSCAN\n",
    "\n",
    "# 자신을 포함하여 특정 거리(eps) 내에 특정 개수(min_samples) 이상의 샘플이 존재하는 샘플을 핵심 샘플로 간주.\n",
    "# 핵심 샘플로부터 특정 거리 이내에 있는 모든 샘플은 동일한 클러스터에 소속됨\n",
    "# 이 이웃에는 다른 핵심 샘플이 포함될 수도 있고, 핵심 샘플들의 연속된 이웃이 하나의 클러스터를 형성할 수도 있음.\n",
    "model = DBSCAN(eps=0.0001,min_samples=5, metric = \"cosine\")\n",
    "# metric: 샘플 간의 거리 계산 식으로는 Cosine distance를 이용\n",
    "result = model.fit_predict(df_temp) # DBSCAN의 .fit_predict() 함수를 사용\n",
    "# DBSCAN 클러스터링만 수행하는 .fit() 메서드와 달리 클러스터를 계산하고 레이블을 예측한다."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1, -1,  2,  3, -1,  4, -1, -1, -1, -1, -1, -1, -1,  0, -1, -1,\n",
       "        0, -1,  1, -1, -1,  0, -1, -1, -1, -1, -1,  0, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1,  0, -1, -1, -1, -1, -1, -1,  0, -1, -1, -1, -1,\n",
       "       -1, -1,  0, -1, -1, -1, -1, -1,  1, -1, -1, -1, -1,  1, -1, -1, -1,\n",
       "       -1,  1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1,  2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1,  2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  2, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1,  2, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1,  3, -1, -1, -1,  3, -1, -1, -1, -1, -1, -1, -1, -1,  3, -1, -1,\n",
       "       -1, -1, -1, -1, -1,  3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  3,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1,  4, -1, -1,  4, -1, -1, -1,  4, -1, -1, -1,\n",
       "       -1,  3, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,  4, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1, -1,\n",
       "       -1, -1, -1, -1, -1, -1], dtype=int64)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1, -1,  2,  3, -1,  4, -1, -1, -1], dtype=int64)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 각 샘플의 레이블: 핵심 샘플들의 인덱스 또는 이상치 인덱스(-1)\n",
    "result[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7.14455300e-01, -5.31384500e-01,  2.48801500e+00,\n",
       "        -7.70629800e-01,  1.12286470e-01,  7.74671300e-01,\n",
       "         2.07088730e+00,  1.77020900e+00, -2.35330630e+00,\n",
       "         1.78303860e+00, -1.52544600e+00, -1.17658100e+00,\n",
       "        -1.10703720e+00,  4.42605560e-01,  1.71463040e+00,\n",
       "        -1.99590410e-01, -1.91826080e+00, -1.49604290e-01,\n",
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       "        -1.89527450e-01],\n",
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       "        -1.26183840e+00,  7.55151600e-01,  9.73670600e-01,\n",
       "         1.41608820e+00,  3.67311720e-01, -1.69271220e+00,\n",
       "        -6.96961400e-01, -1.18139830e+00, -1.41306040e+00,\n",
       "        -1.64503960e+00, -4.44137360e-01, -8.72764800e-01,\n",
       "         1.53474870e-01,  7.30371900e-02,  1.13659830e+00,\n",
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       "         2.29380350e+00,  1.10108540e+00, -8.27426900e-01,\n",
       "         4.65677830e-02],\n",
       "       [ 5.97797100e-01, -4.11963370e+00,  4.07138630e+00,\n",
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       "        -2.15290570e+00,  1.10099080e+00, -3.77268800e+00,\n",
       "        -1.06617890e+00,  3.61754270e+00,  2.61033270e+00,\n",
       "         8.89510630e-01,  1.67590000e+00,  1.08041850e+00,\n",
       "        -2.54857730e+00, -1.40432750e+00,  1.55440500e+00,\n",
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       "        -1.02765920e+00, -1.34171920e+00, -1.92391180e+00,\n",
       "        -1.22335790e+00,  1.95448180e+00, -1.88688730e+00,\n",
       "         1.82063340e+00, -1.96409700e-01, -2.82522170e-01,\n",
       "        -2.11703600e+00,  1.16534440e+00,  1.07421780e+00,\n",
       "         3.27373150e+00,  1.06248210e+00,  5.84409950e-01,\n",
       "        -1.64070650e+00],\n",
       "       [-2.45545120e-01, -2.35073110e+00,  2.95262430e-01,\n",
       "        -3.23797230e-01, -2.77038460e-01, -1.59475370e-01,\n",
       "        -8.95428960e-01,  1.13952240e+00, -1.00469950e+00,\n",
       "        -2.66035370e-03, -2.81917400e-03,  1.79597970e-01,\n",
       "         7.51906700e-01, -8.24816900e-02,  7.31157300e-01,\n",
       "         3.02036170e-01, -1.31693520e+00, -8.44635700e-02,\n",
       "         1.05130920e+00, -1.20017090e+00, -5.88324400e-01,\n",
       "        -1.39841820e+00, -8.88618600e-01,  5.62640000e-01,\n",
       "        -5.07979750e-01,  3.28461080e-02,  6.32792100e-01,\n",
       "         1.36271580e-01,  5.34113500e-01, -7.81055500e-01,\n",
       "         3.16578600e+00,  2.26934240e+00, -4.32737020e-01,\n",
       "         1.62403060e+00,  1.64696590e+00,  4.79190830e-01,\n",
       "        -6.54859540e-01, -3.73314050e-01,  1.96590350e+00,\n",
       "        -5.31077000e-01,  2.33046150e+00,  2.53845240e+00,\n",
       "         2.46292350e+00,  2.02892740e-01,  3.46207500e-01,\n",
       "        -6.59425560e-01, -3.97221480e-01, -1.27993380e+00,\n",
       "        -6.87028500e-01, -1.94939760e-01, -3.41639520e-01,\n",
       "         7.13047400e-01, -7.01453570e-01,  5.05608500e-01,\n",
       "        -1.24926360e+00,  5.55181200e-01, -3.59650050e-01,\n",
       "         9.17855860e-01, -5.68563640e-01,  9.42104760e-01,\n",
       "        -2.14157500e+00,  1.98813830e+00, -4.11671940e-01,\n",
       "        -5.57375130e-01,  3.14543140e-02,  4.17896630e-01,\n",
       "         6.75444200e-01,  8.00340800e-01,  4.82971300e-01,\n",
       "        -6.57042000e-01, -1.49706570e+00,  1.29366980e+00,\n",
       "         1.32028960e+00,  1.16529290e+00, -1.11987720e+00,\n",
       "         1.42034330e+00,  1.41466080e-01,  4.45823550e-01,\n",
       "         6.52396400e-01,  1.25582440e+00,  2.72232220e+00,\n",
       "         4.57666280e-01,  7.99892370e-01, -4.08458530e-01,\n",
       "        -1.76898360e+00, -1.41587870e+00,  8.57804360e-01,\n",
       "        -1.68346360e+00,  8.59136600e-01, -6.83276900e-01,\n",
       "         1.53841540e+00, -3.08162480e-01,  4.67864100e-01,\n",
       "        -5.47525050e-01, -1.75750450e-01, -3.92333500e-01,\n",
       "         1.10760570e+00,  1.39305940e+00, -8.09274260e-01,\n",
       "         1.07123150e+00]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 핵심 샘플들\n",
    "model.components_[:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  3,  4,  6, 14, 17, 19, 22, 28], dtype=int64)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 핵심 샘플들의 인덱스\n",
    "model.core_sample_indices_[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['result'] = result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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>Unnamed: 0</th>\n",
       "      <th>Document</th>\n",
       "      <th>tokenizedDocument</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>...</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",
       "      <th>tokenized_data</th>\n",
       "      <th>result</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>운영체제 서비스 (Operating System Services)</td>\n",
       "      <td>운영체제 서비스</td>\n",
       "      <td>0.714455</td>\n",
       "      <td>-0.531385</td>\n",
       "      <td>2.488015</td>\n",
       "      <td>-0.770630</td>\n",
       "      <td>0.112286</td>\n",
       "      <td>0.774671</td>\n",
       "      <td>2.070887</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.708204</td>\n",
       "      <td>-0.606940</td>\n",
       "      <td>-1.245999</td>\n",
       "      <td>1.037042</td>\n",
       "      <td>0.395951</td>\n",
       "      <td>0.475926</td>\n",
       "      <td>0.371759</td>\n",
       "      <td>-1.622231</td>\n",
       "      <td>[운영체제, 서비스]</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
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       "      <td>운영체제 사용자 인터페이스</td>\n",
       "      <td>1.373194</td>\n",
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       "      <td>2.041227</td>\n",
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       "      <td>0.202811</td>\n",
       "      <td>0.251074</td>\n",
       "      <td>0.367272</td>\n",
       "      <td>...</td>\n",
       "      <td>-2.177040</td>\n",
       "      <td>-1.377887</td>\n",
       "      <td>0.282307</td>\n",
       "      <td>1.705836</td>\n",
       "      <td>1.317941</td>\n",
       "      <td>1.302224</td>\n",
       "      <td>-1.182694</td>\n",
       "      <td>-0.189527</td>\n",
       "      <td>[운영체제, 사용자, 인터페이스]</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>시스템 콜 (System Calls)</td>\n",
       "      <td>시스템 콜</td>\n",
       "      <td>0.011394</td>\n",
       "      <td>-0.826875</td>\n",
       "      <td>2.973761</td>\n",
       "      <td>-0.366313</td>\n",
       "      <td>-0.839046</td>\n",
       "      <td>0.789231</td>\n",
       "      <td>0.961213</td>\n",
       "      <td>...</td>\n",
       "      <td>1.141711</td>\n",
       "      <td>-1.204290</td>\n",
       "      <td>-0.019782</td>\n",
       "      <td>0.544513</td>\n",
       "      <td>2.611695</td>\n",
       "      <td>1.183234</td>\n",
       "      <td>-0.109376</td>\n",
       "      <td>-0.170399</td>\n",
       "      <td>[시스템, 콜]</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <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>-0.314897</td>\n",
       "      <td>-1.808564</td>\n",
       "      <td>2.901775</td>\n",
       "      <td>-0.102677</td>\n",
       "      <td>-0.380242</td>\n",
       "      <td>0.683322</td>\n",
       "      <td>0.860649</td>\n",
       "      <td>...</td>\n",
       "      <td>1.136598</td>\n",
       "      <td>-1.001038</td>\n",
       "      <td>0.101517</td>\n",
       "      <td>0.996975</td>\n",
       "      <td>2.293804</td>\n",
       "      <td>1.101085</td>\n",
       "      <td>-0.827427</td>\n",
       "      <td>0.046568</td>\n",
       "      <td>[시스템, 콜, 유형]</td>\n",
       "      <td>2</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>0.597797</td>\n",
       "      <td>-4.119634</td>\n",
       "      <td>4.071386</td>\n",
       "      <td>-0.778798</td>\n",
       "      <td>-0.830832</td>\n",
       "      <td>0.947624</td>\n",
       "      <td>2.868076</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.282522</td>\n",
       "      <td>-2.117036</td>\n",
       "      <td>1.165344</td>\n",
       "      <td>1.074218</td>\n",
       "      <td>3.273731</td>\n",
       "      <td>1.062482</td>\n",
       "      <td>0.584410</td>\n",
       "      <td>-1.640707</td>\n",
       "      <td>[시스템, 프로그램]</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 105 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   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",
       "  tokenizedDocument       dm1       dm2       dm3       dm4       dm5  \\\n",
       "0          운영체제 서비스  0.714455 -0.531385  2.488015 -0.770630  0.112286   \n",
       "1    운영체제 사용자 인터페이스  1.373194 -2.268541  2.041227 -1.548548  0.202811   \n",
       "2             시스템 콜  0.011394 -0.826875  2.973761 -0.366313 -0.839046   \n",
       "3          시스템 콜 유형 -0.314897 -1.808564  2.901775 -0.102677 -0.380242   \n",
       "4          시스템 프로그램  0.597797 -4.119634  4.071386 -0.778798 -0.830832   \n",
       "\n",
       "        dm6       dm7  ...      dm93      dm94      dm95      dm96      dm97  \\\n",
       "0  0.774671  2.070887  ... -0.708204 -0.606940 -1.245999  1.037042  0.395951   \n",
       "1  0.251074  0.367272  ... -2.177040 -1.377887  0.282307  1.705836  1.317941   \n",
       "2  0.789231  0.961213  ...  1.141711 -1.204290 -0.019782  0.544513  2.611695   \n",
       "3  0.683322  0.860649  ...  1.136598 -1.001038  0.101517  0.996975  2.293804   \n",
       "4  0.947624  2.868076  ... -0.282522 -2.117036  1.165344  1.074218  3.273731   \n",
       "\n",
       "       dm98      dm99     dm100      tokenized_data  result  \n",
       "0  0.475926  0.371759 -1.622231         [운영체제, 서비스]       0  \n",
       "1  1.302224 -1.182694 -0.189527  [운영체제, 사용자, 인터페이스]       1  \n",
       "2  1.183234 -0.109376 -0.170399            [시스템, 콜]      -1  \n",
       "3  1.101085 -0.827427  0.046568        [시스템, 콜, 유형]       2  \n",
       "4  1.062482  0.584410 -1.640707         [시스템, 프로그램]       3  \n",
       "\n",
       "[5 rows x 105 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cluster num : 1\n",
      " 운영체제 사용자 인터페이스 (User Operating System Interface)\n",
      "사용자 인터페이스\n",
      "사용자 인터페이스 – CLI\n",
      "사용자 인터페이스 – CLI\n",
      "사용자 인터페이스 – GUI\n",
      "\n",
      "cluster num : 2\n",
      " 시스템 콜 유형 (Types of System Calls)\n",
      "시스템 콜 유형  \n",
      "시스템 콜 유형  \n",
      "시스템 콜 유형  \n",
      "시스템 콜 유형  \n",
      "\n",
      "cluster num : 3\n",
      " 시스템 프로그램 (System Programs)\n",
      "시스템 프로그램 (System Programs)  \n",
      "시스템 프로그램 (System Programs)  \n",
      "시스템 프로그램 (System Programs)  \n",
      "시스템 프로그램 (System Programs)  \n",
      "시스템 프로그램 (System Programs)  \n",
      " 시스템 프로그램\n",
      "\n",
      "cluster num : 4\n",
      " 운영체제 구조 (Operating System Structure)\n",
      "운영체제 구조 (structure)\n",
      "간단한 구조 -- MS-DOS\n",
      "좀더 복잡한 구조 - UNIX\n",
      "Android Architecture (구조)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for cluster_num in set(result):\n",
    "    # -1,0은 노이즈 판별이 났거나 클러스터링이 안된 경우\n",
    "    if(cluster_num == -1 or cluster_num == 0): \n",
    "        continue\n",
    "    else:\n",
    "        print(\"cluster num : {}\".format(cluster_num))\n",
    "        temp_df = df[df['result'] == cluster_num] # cluster num 별로 조회\n",
    "        for title in temp_df['Document']:\n",
    "            print(title) # 토큰별로 나타내도록 한다.\n",
    "        print()"
   ]
  }
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