StatsAnalysis_6. 判别分析与聚类分析R代码.ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 1. R语言的mahalannobis函数返回的马氏距离的平方\n",
    "- ##### R关于马氏距离的函数\n",
    "![6.%E5%88%A4%E5%88%AB%E5%88%86%E6%9E%905_3.jpg](attachment:6.%E5%88%A4%E5%88%AB%E5%88%86%E6%9E%905_3.jpg)\n",
    "- ##### 很显然，R语言中各种判别算法都是使用马氏距离的平方。\n",
    "\n",
    "\n",
    "- ##### 实验证明，如果使用真实马氏距离，即开平方的马氏距离，各种判别算法的准确率会显著降低。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>2.13446792332484</li><li>2.84911868615858</li><li>2.08133866395779</li><li>2.45238163167967</li><li>2.46215453471405</li><li>3.8834176695537</li><li>2.86210812950982</li><li>1.83330031712726</li><li>3.38407311463747</li><li>2.37521788559178</li><li>3.28310687463554</li><li>2.77479750718393</li><li>2.61329751702606</li><li>3.6034323522489</li><li>8.73751836222675</li><li>9.71278989439061</li><li>5.76058767783533</li><li>2.3213894420764</li><li>4.49968993052414</li><li>3.43886582892008</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2.13446792332484\n",
       "\\item 2.84911868615858\n",
       "\\item 2.08133866395779\n",
       "\\item 2.45238163167967\n",
       "\\item 2.46215453471405\n",
       "\\item 3.8834176695537\n",
       "\\item 2.86210812950982\n",
       "\\item 1.83330031712726\n",
       "\\item 3.38407311463747\n",
       "\\item 2.37521788559178\n",
       "\\item 3.28310687463554\n",
       "\\item 2.77479750718393\n",
       "\\item 2.61329751702606\n",
       "\\item 3.6034323522489\n",
       "\\item 8.73751836222675\n",
       "\\item 9.71278989439061\n",
       "\\item 5.76058767783533\n",
       "\\item 2.3213894420764\n",
       "\\item 4.49968993052414\n",
       "\\item 3.43886582892008\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2.13446792332484\n",
       "2. 2.84911868615858\n",
       "3. 2.08133866395779\n",
       "4. 2.45238163167967\n",
       "5. 2.46215453471405\n",
       "6. 3.8834176695537\n",
       "7. 2.86210812950982\n",
       "8. 1.83330031712726\n",
       "9. 3.38407311463747\n",
       "10. 2.37521788559178\n",
       "11. 3.28310687463554\n",
       "12. 2.77479750718393\n",
       "13. 2.61329751702606\n",
       "14. 3.6034323522489\n",
       "15. 8.73751836222675\n",
       "16. 9.71278989439061\n",
       "17. 5.76058767783533\n",
       "18. 2.3213894420764\n",
       "19. 4.49968993052414\n",
       "20. 3.43886582892008\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 2.134468 2.849119 2.081339 2.452382 2.462155 3.883418 2.862108 1.833300\n",
       " [9] 3.384073 2.375218 3.283107 2.774798 2.613298 3.603432 8.737518 9.712790\n",
       "[17] 5.760588 2.321389 4.499690 3.438866"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###使用iris计算马氏距离\n",
    "data <- iris[1:4]\n",
    "#m = mean(data)\n",
    "m <- colMeans(data)\n",
    "cov1 <- cov(data)\n",
    "maha <- mahalanobis(data,m,cov1)\n",
    "maha[1:20]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>2.13446792332484</li><li>2.84911868615858</li><li>2.08133866395779</li><li>2.45238163167967</li><li>2.46215453471405</li><li>3.8834176695537</li><li>2.86210812950982</li><li>1.83330031712726</li><li>3.38407311463747</li><li>2.37521788559178</li><li>3.28310687463554</li><li>2.77479750718393</li><li>2.61329751702606</li><li>3.6034323522489</li><li>8.73751836222675</li><li>9.71278989439061</li><li>5.76058767783533</li><li>2.3213894420764</li><li>4.49968993052414</li><li>3.43886582892008</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2.13446792332484\n",
       "\\item 2.84911868615858\n",
       "\\item 2.08133866395779\n",
       "\\item 2.45238163167967\n",
       "\\item 2.46215453471405\n",
       "\\item 3.8834176695537\n",
       "\\item 2.86210812950982\n",
       "\\item 1.83330031712726\n",
       "\\item 3.38407311463747\n",
       "\\item 2.37521788559178\n",
       "\\item 3.28310687463554\n",
       "\\item 2.77479750718393\n",
       "\\item 2.61329751702606\n",
       "\\item 3.6034323522489\n",
       "\\item 8.73751836222675\n",
       "\\item 9.71278989439061\n",
       "\\item 5.76058767783533\n",
       "\\item 2.3213894420764\n",
       "\\item 4.49968993052414\n",
       "\\item 3.43886582892008\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2.13446792332484\n",
       "2. 2.84911868615858\n",
       "3. 2.08133866395779\n",
       "4. 2.45238163167967\n",
       "5. 2.46215453471405\n",
       "6. 3.8834176695537\n",
       "7. 2.86210812950982\n",
       "8. 1.83330031712726\n",
       "9. 3.38407311463747\n",
       "10. 2.37521788559178\n",
       "11. 3.28310687463554\n",
       "12. 2.77479750718393\n",
       "13. 2.61329751702606\n",
       "14. 3.6034323522489\n",
       "15. 8.73751836222675\n",
       "16. 9.71278989439061\n",
       "17. 5.76058767783533\n",
       "18. 2.3213894420764\n",
       "19. 4.49968993052414\n",
       "20. 3.43886582892008\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 2.134468 2.849119 2.081339 2.452382 2.462155 3.883418 2.862108 1.833300\n",
       " [9] 3.384073 2.375218 3.283107 2.774798 2.613298 3.603432 8.737518 9.712790\n",
       "[17] 5.760588 2.321389 4.499690 3.438866"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>2.13446792332484</li><li>2.84911868615858</li><li>2.08133866395779</li><li>2.45238163167967</li><li>2.46215453471405</li><li>3.8834176695537</li><li>2.86210812950982</li><li>1.83330031712726</li><li>3.38407311463747</li><li>2.37521788559178</li><li>3.28310687463554</li><li>2.77479750718393</li><li>2.61329751702606</li><li>3.6034323522489</li><li>8.73751836222675</li><li>9.71278989439061</li><li>5.76058767783533</li><li>2.3213894420764</li><li>4.49968993052414</li><li>3.43886582892008</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 2.13446792332484\n",
       "\\item 2.84911868615858\n",
       "\\item 2.08133866395779\n",
       "\\item 2.45238163167967\n",
       "\\item 2.46215453471405\n",
       "\\item 3.8834176695537\n",
       "\\item 2.86210812950982\n",
       "\\item 1.83330031712726\n",
       "\\item 3.38407311463747\n",
       "\\item 2.37521788559178\n",
       "\\item 3.28310687463554\n",
       "\\item 2.77479750718393\n",
       "\\item 2.61329751702606\n",
       "\\item 3.6034323522489\n",
       "\\item 8.73751836222675\n",
       "\\item 9.71278989439061\n",
       "\\item 5.76058767783533\n",
       "\\item 2.3213894420764\n",
       "\\item 4.49968993052414\n",
       "\\item 3.43886582892008\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 2.13446792332484\n",
       "2. 2.84911868615858\n",
       "3. 2.08133866395779\n",
       "4. 2.45238163167967\n",
       "5. 2.46215453471405\n",
       "6. 3.8834176695537\n",
       "7. 2.86210812950982\n",
       "8. 1.83330031712726\n",
       "9. 3.38407311463747\n",
       "10. 2.37521788559178\n",
       "11. 3.28310687463554\n",
       "12. 2.77479750718393\n",
       "13. 2.61329751702606\n",
       "14. 3.6034323522489\n",
       "15. 8.73751836222675\n",
       "16. 9.71278989439061\n",
       "17. 5.76058767783533\n",
       "18. 2.3213894420764\n",
       "19. 4.49968993052414\n",
       "20. 3.43886582892008\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       " [1] 2.134468 2.849119 2.081339 2.452382 2.462155 3.883418 2.862108 1.833300\n",
       " [9] 3.384073 2.375218 3.283107 2.774798 2.613298 3.603432 8.737518 9.712790\n",
       "[17] 5.760588 2.321389 4.499690 3.438866"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###R语言计算马氏距离平方的代码\n",
    "x=x <- sweep(data, 2L, m)\n",
    "x<-as.matrix(x)\n",
    "###只显示前20个\n",
    "#%*%求点积， *是逐元素相乘\n",
    "#下面两种算法结果相同\n",
    "rowSums(x %*% solve(cov1) * x)[1:20]\n",
    "diag(x %*% solve(cov1) %*% t(x))[1:20]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 2. 二分类和多分类贝叶斯判别"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "###贝叶斯多分类判别\n",
    "distinguish.bayes<-function\n",
    "   (TrnX, TrnG, p=rep(1, length(levels(TrnG))), \n",
    "    TstX = NULL, var.equal = FALSE){\n",
    "   if ( is.factor(TrnG) == FALSE){\n",
    "       mx<-nrow(TrnX); mg<-nrow(TrnG)\n",
    "       TrnX<-rbind(TrnX, TrnG)\n",
    "       TrnG<-factor(rep(1:2, c(mx, mg)))\n",
    "   }\n",
    "   if (is.null(TstX) == TRUE) TstX<-TrnX\n",
    "   if (is.vector(TstX) == TRUE)  TstX<-t(as.matrix(TstX))\n",
    "   else if (is.matrix(TstX) != TRUE)\n",
    "      TstX<-as.matrix(TstX)\n",
    "   if (is.matrix(TrnX) != TRUE) TrnX<-as.matrix(TrnX)\n",
    "\n",
    "   nx<-nrow(TstX)\n",
    "   blong<-matrix(rep(0, nx), nrow=1, dimnames=list(\"blong\", 1:nx))\n",
    "   g<-length(levels(TrnG))\n",
    "   mu<-matrix(0, nrow=g, ncol=ncol(TrnX))\n",
    "   for (i in 1:g)\n",
    "      mu[i,]<-colMeans(TrnX[TrnG==i,]) \n",
    "   D<-matrix(0, nrow=g, ncol=nx)\n",
    "   if (var.equal == TRUE  || var.equal == T){\n",
    "      for (i in 1:g){\n",
    "         d2 <- mahalanobis(TstX, mu[i,], var(TrnX))\n",
    "         D[i,] <- d2 - 2*log(p[i])\n",
    "      }\n",
    "   }\n",
    "   else{\n",
    "      for (i in 1:g){\n",
    "         S<-var(TrnX[TrnG==i,])\n",
    "         d2 <- mahalanobis(TstX, mu[i,], S)\n",
    "         D[i,] <- d2 - 2*log(p[i])-log(det(S))\n",
    "      }\n",
    "   }\n",
    "   for (j in 1:nx){\n",
    "      dmin<-Inf\n",
    "      for (i in 1:g)\n",
    "          if (D[i,j]<dmin){\n",
    "             dmin<-D[i,j]; blong[j]<-i\n",
    "      }\n",
    "   }\n",
    "   blong\n",
    "}\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "###二分类贝叶斯判别\n",
    "discriminiant.bayes<-function\n",
    "   (TrnX1, TrnX2, rate=1, TstX = NULL, var.equal = FALSE){\n",
    "   if (is.null(TstX) == TRUE) TstX<-rbind(TrnX1,TrnX2)\n",
    "   if (is.vector(TstX) == TRUE)  TstX<-t(as.matrix(TstX))\n",
    "   else if (is.matrix(TstX) != TRUE)\n",
    "      TstX<-as.matrix(TstX)\n",
    "   if (is.matrix(TrnX1) != TRUE) TrnX1<-as.matrix(TrnX1)\n",
    "   if (is.matrix(TrnX2) != TRUE) TrnX2<-as.matrix(TrnX2)\n",
    "\n",
    "   nx<-nrow(TstX)\n",
    "   blong<-matrix(rep(0, nx), nrow=1, byrow=TRUE, \n",
    "         dimnames=list(\"blong\", 1:nx))\n",
    "   mu1<-colMeans(TrnX1); mu2<-colMeans(TrnX2) \n",
    "   if (var.equal == TRUE  || var.equal == T){\n",
    "      S<-var(rbind(TrnX1,TrnX2)); beta<-2*log(rate)\n",
    "      w<-mahalanobis(TstX, mu2, S)-mahalanobis(TstX, mu1, S)\n",
    "   }\n",
    "   else{\n",
    "      S1<-var(TrnX1); S2<-var(TrnX2)\n",
    "      beta<-2*log(rate)+log(det(S1)/det(S2))\n",
    "      w<-mahalanobis(TstX, mu2, S2)-mahalanobis(TstX, mu1, S1)\n",
    "   }\n",
    "   \n",
    "   for (i in 1:nx){\n",
    "      if (w[i]>beta)\n",
    "          blong[i]<-1\n",
    "      else\n",
    "          blong[i]<-2\n",
    "   }\n",
    "   blong\n",
    "}\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "0.93"
      ],
      "text/latex": [
       "0.93"
      ],
      "text/markdown": [
       "0.93"
      ],
      "text/plain": [
       "[1] 0.93"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "4"
      ],
      "text/latex": [
       "4"
      ],
      "text/markdown": [
       "4"
      ],
      "text/plain": [
       "[1] 4"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 14 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>1</th><th scope=col>2</th><th scope=col>3</th><th scope=col>4</th><th scope=col>5</th><th scope=col>6</th><th scope=col>7</th><th scope=col>8</th><th scope=col>9</th><th scope=col>10</th><th scope=col>11</th><th scope=col>12</th><th scope=col>13</th><th scope=col>14</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>blong</th><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 14 of type dbl\n",
       "\\begin{tabular}{r|llllllllllllll}\n",
       "  & 1 & 2 & 3 & 4 & 5 & 6 & 7 & 8 & 9 & 10 & 11 & 12 & 13 & 14\\\\\n",
       "\\hline\n",
       "\tblong & 1 & 1 & 1 & 1 & 1 & 1 & 2 & 2 & 2 & 2 & 2 & 2 & 2 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 14 of type dbl\n",
       "\n",
       "| <!--/--> | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 | 13 | 14 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| blong | 1 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 2 | 2 | 2 | 2 | 2 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "      1 2 3 4 5 6 7 8 9 10 11 12 13 14\n",
       "blong 1 1 1 1 1 1 2 2 2 2  2  2  2  2 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###二分类贝叶斯判别示例\n",
    "TrnX1<-matrix(\n",
    "   c(24.8, 24.1, 26.6, 23.5, 25.5, 27.4, \n",
    "     -2.0, -2.4, -3.0, -1.9, -2.1, -3.1),\n",
    "   ncol=2)\n",
    "TrnX2<-matrix(\n",
    "   c(22.1, 21.6, 22.0, 22.8, 22.7, 21.5, 22.1, 21.4, \n",
    "     -0.7, -1.4, -0.8, -1.6, -1.5, -1.0, -1.2, -1.3),\n",
    "   ncol=2)\n",
    "\n",
    "#### 样本协方差相同\n",
    "results <- discriminiant.bayes(TrnX1, TrnX2, rate=8/6, var.equal=TRUE)\n",
    "classes <- rep(c(1,2),c(6,8))\n",
    "round(sum(results==classes)/length(classes),2)\n",
    "which(results!=classes)\n",
    "#### 样本协方差不同\n",
    "discriminiant.bayes(TrnX1, TrnX2, rate=8/6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3. R语言的二次判别分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"二次判别对鸢尾花分类的准确率： 0.98\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>71</li><li>84</li><li>134</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 71\n",
       "\\item 84\n",
       "\\item 134\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 71\n",
       "2. 84\n",
       "3. 134\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  71  84 134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "###使用qda计算鸢尾花的分类。\n",
    "#head(iris)\n",
    "library(MASS)\n",
    "qda.fit=qda(iris$Species~iris$Sepal.Length+iris$Sepal.Width+\n",
    "              iris$Petal.Length+iris$Petal.Width\n",
    "            ,data=iris)\n",
    "qda.class=predict(qda.fit,iris[1:4])$class\n",
    "\n",
    "p<-sum(qda.class==iris$Species)/dim(iris)[1]\n",
    "print(paste('二次判别对鸢尾花分类的准确率：',p))\n",
    "which(qda.class!=iris$Species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"线性判别分析对鸢尾花分类的准确率： 0.98\"\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>\n",
       ".list-inline {list-style: none; margin:0; padding: 0}\n",
       ".list-inline>li {display: inline-block}\n",
       ".list-inline>li:not(:last-child)::after {content: \"\\00b7\"; padding: 0 .5ex}\n",
       "</style>\n",
       "<ol class=list-inline><li>71</li><li>84</li><li>134</li></ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 71\n",
       "\\item 84\n",
       "\\item 134\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 71\n",
       "2. 84\n",
       "3. 134\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]  71  84 134"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "lda.fit=lda(iris$Species~iris$Sepal.Length+iris$Sepal.Width+\n",
    "              iris$Petal.Length+iris$Petal.Width\n",
    "            ,data=iris)\n",
    "lda.class=predict(lda.fit,iris[1:4])$class\n",
    "\n",
    "p1<-sum(lda.class==iris$Species)/dim(iris)[1]\n",
    "print(paste('线性判别分析对鸢尾花分类的准确率：',p1))\n",
    "which(lda.class!=iris$Species)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 4. 国民经济数据聚类分析 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "The \"ward\" method has been renamed to \"ward.D\"; note new \"ward.D2\"\n",
      "\n",
      "Warning message:\n",
      "\"'plclust' is deprecated.\n",
      "Use 'plot' instead.\n",
      "See help(\"Deprecated\")\"\n",
      "Warning message:\n",
      "\"'plclust' is deprecated.\n",
      "Use 'plot' instead.\n",
      "See help(\"Deprecated\")\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "Plot with title \"Cluster Dendrogram\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"'plclust' is deprecated.\n",
      "Use 'plot' instead.\n",
      "See help(\"Deprecated\")\"\n",
      "Warning message:\n",
      "\"'plclust' is deprecated.\n",
      "Use 'plot' instead.\n",
      "See help(\"Deprecated\")\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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FsP4Kxc98EC05V6TEisqgZmJfxEKO/o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\nSUVORK5CYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "K-means clustering with 5 clusters of sizes 4, 3, 16, 1, 7\n",
       "\n",
       "Cluster means:\n",
       "          x1          x2         x3         x4          x5         x6\n",
       "1  0.2029830 -1.53019285 -0.6594861 -1.0978219  0.05751333 -0.4270452\n",
       "2  1.8790347  1.02836873  2.1203833  2.1727806  1.49972764  2.2232050\n",
       "3 -0.6867323 -0.05815552 -0.4787096 -0.1598851 -0.57749718 -0.5070907\n",
       "4  1.8042004 -1.12776493  0.9368961  1.2959544  3.90904835  1.6014419\n",
       "5  0.3906401  0.72770263  0.4284646 -0.1235496  0.08595291  0.2215108\n",
       "           x7         x8\n",
       "1  0.33154520 -0.2336878\n",
       "2  0.95830640  1.9453274\n",
       "3 -0.49317064 -0.6033238\n",
       "4  3.88031413  2.0187653\n",
       "5 -0.02724055  0.3904549\n",
       "\n",
       "Clustering vector:\n",
       "  北京   天津   河北   山西 内蒙古   辽宁   吉林 黑龙江   上海   江苏   浙江 \n",
       "     2      5      3      3      3      3      3      3      2      5      2 \n",
       "  安徽   福建   江西   山东   河南   湖北   湖南   广东   广西   海南   重庆 \n",
       "     3      1      1      5      3      3      5      4      1      1      5 \n",
       "  四川   贵州   云南   西藏   陕西   甘肃   青海   宁夏   新疆 \n",
       "     3      3      5      5      3      3      3      3      3 \n",
       "\n",
       "Within cluster sum of squares by cluster:\n",
       "[1]  9.035662 10.191335 20.384632  0.000000 23.259407\n",
       " (between_SS / total_SS =  73.8 %)\n",
       "\n",
       "Available components:\n",
       "\n",
       "[1] \"cluster\"      \"centers\"      \"totss\"        \"withinss\"     \"tot.withinss\"\n",
       "[6] \"betweenss\"    \"size\"         \"iter\"         \"ifault\"      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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PVDq6eW/cOEbtdutezVWM7xj1jXbsWMKeSsQkWoOlbqbUV4vWs3meBVM2VT7VaK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\nYII=",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X<-data.frame(\n",
    "   x1=c(2959.19, 2459.77, 1495.63, 1046.33, 1303.97, 1730.84, \n",
    "        1561.86, 1410.11, 3712.31, 2207.58, 2629.16, 1844.78, \n",
    "        2709.46, 1563.78, 1675.75, 1427.65, 1783.43, 1942.23, \n",
    "        3055.17, 2033.87, 2057.86, 2303.29, 1974.28, 1673.82, \n",
    "        2194.25, 2646.61, 1472.95, 1525.57, 1654.69, 1375.46, \n",
    "        1608.82), \n",
    "   x2=c(730.79, 495.47, 515.90, 477.77, 524.29, 553.90, 492.42, \n",
    "        510.71, 550.74, 449.37, 557.32, 430.29, 428.11, 303.65, \n",
    "        613.32, 431.79, 511.88, 512.27, 353.23, 300.82, 186.44, \n",
    "        589.99, 507.76, 437.75, 537.01, 839.70, 390.89, 472.98, \n",
    "        437.77, 480.99, 536.05),\n",
    "   x3=c(749.41, 697.33, 362.37, 290.15, 254.83, 246.91, 200.49, \n",
    "        211.88, 893.37, 572.40, 689.73, 271.28, 334.12, 233.81, \n",
    "        550.71, 288.55, 282.84, 401.39, 564.56, 338.65, 202.72, \n",
    "        516.21, 344.79, 461.61, 369.07, 204.44, 447.95, 328.90, \n",
    "        258.78, 273.84, 432.46), \n",
    "   x4=c(513.34, 302.87, 285.32, 208.57, 192.17, 279.81, 218.36, \n",
    "        277.11, 346.93, 211.92, 435.69, 126.33, 160.77, 107.90, \n",
    "        219.79, 208.14, 201.01, 206.06, 356.27, 157.78, 171.79, \n",
    "        236.55, 203.21, 153.32, 249.54, 209.11, 259.51, 219.86, \n",
    "        303.00, 317.32, 235.82),\n",
    "   x5=c(467.87, 284.19, 272.95, 201.50, 249.81, 239.18, 220.69, \n",
    "        224.65, 527.00, 302.09, 514.66, 250.56, 405.14, 209.70, \n",
    "        272.59, 217.00, 237.60, 321.29, 811.88, 329.06, 329.65, \n",
    "        403.92, 240.24, 254.66, 290.84, 379.30, 230.61, 206.65, \n",
    "        244.93, 251.08, 250.28),\n",
    "   x6=c(1141.82, 735.97, 540.58, 414.72, 463.09, 445.20, 459.62, \n",
    "        376.82, 1034.98, 585.23, 795.87, 513.18, 461.67, 393.99, \n",
    "        599.43, 337.76, 617.74, 697.22, 873.06, 621.74, 477.17, \n",
    "        730.05, 575.10, 445.59, 561.91, 371.04, 490.90, 449.69, \n",
    "        479.53, 424.75, 541.30),\n",
    "   x7=c(478.42, 570.84, 364.91, 281.84, 287.87, 330.24, 360.48, \n",
    "        317.61, 720.33, 429.77, 575.76, 314.00, 535.13, 509.39, \n",
    "        371.62, 421.31, 523.52, 492.60, 1082.82, 587.02, 312.93, \n",
    "        438.41, 430.36, 346.11, 407.70, 269.59, 469.10, 249.66, \n",
    "        288.56, 228.73, 344.85),\n",
    "   x8=c(457.64, 305.08, 188.63, 212.10, 192.96, 163.86, 147.76, \n",
    "        152.85, 462.03, 252.54, 323.36, 151.39, 232.29, 160.12, \n",
    "        211.84, 165.32, 182.52, 226.45, 420.81, 218.27, 279.19, \n",
    "        225.80, 223.46, 191.48, 330.95, 389.33, 191.34, 228.19, \n",
    "        236.51, 195.93, 214.40),\n",
    "   row.names = c(\"北京\", \"天津\", \"河北\", \"山西\", \"内蒙古\", \"辽宁\", \"吉林\", \n",
    "                   \"黑龙江\", \"上海\", \"江苏\", \"浙江\", \"安徽\", \"福建\", \"江西\", \n",
    "                   \"山东\",  \"河南\", \"湖北\", \"湖南\", \"广东\", \"广西\", \"海南\", \n",
    "                   \"重庆\", \"四川\", \"贵州\", \"云南\", \"西藏\", \"陕西\", \"甘肃\", \n",
    "                   \"青海\", \"宁夏\", \"新疆\")\n",
    ")\n",
    "Province<-dist(scale(X))\n",
    "#scale(X)\n",
    "hc1<-hclust(Province, \"complete\")\n",
    "hc2<-hclust(Province, \"average\")\n",
    "hc3<-hclust(Province, \"centroid\")\n",
    "hc4<-hclust(Province, \"ward\")\n",
    "plot(hc1,hang=-1)\n",
    "\n",
    "opar<-par(mfrow=c(2,1), mar=c(5.2,4,0,0))\n",
    "plclust(hc1,hang=-1)\n",
    "re1<-rect.hclust(hc1,k=5,border=\"red\")\n",
    "plclust(hc2,hang=-1)\n",
    "re2<-rect.hclust(hc2,k=5,border=\"red\")\n",
    "par(opar)\n",
    "\n",
    "opar<-par(mfrow=c(2,1), mar=c(5.2,4,0,0))\n",
    "plclust(hc3,hang=-1)\n",
    "re3<-rect.hclust(hc3,k=5,border=\"red\")\n",
    "plclust(hc4,hang=-1)\n",
    "re4<-rect.hclust(hc4,k=5,border=\"red\")\n",
    "par(opar)\n",
    "\n",
    "kmeans(scale(X),5,nstart=20)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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