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chapters/ch11_cluster.html

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+<link href="../chapters/ch12_cluster.html" rel="next">
 <link href="../index.html" rel="prev">
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@@ -145,6 +146,11 @@
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   <a href="../chapters/ch11_cluster.html" class="sidebar-item-text sidebar-link active"><span class="chapter-title">11장 군집분석 개요</span></a>
   </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="../chapters/ch12_cluster.html" class="sidebar-item-text sidebar-link"><span class="chapter-title">12장 계층적 군집방법</span></a>
+  </div>
 </li>
     </ul>
     </div>
@@ -537,6 +543,9 @@ <h2 class="anchored" data-anchor-id="예-11.3">(예 11.3)</h2>
       </a>          
   </div>
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+      <a href="../chapters/ch12_cluster.html" class="pagination-link">
+        <span class="nav-page-text"><span class="chapter-title">12장 계층적 군집방법</span></span> <i class="bi bi-arrow-right-short"></i>
+      </a>
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 </nav>
 </div> <!-- /content -->

index.html

@@ -113,6 +113,11 @@
   <div class="sidebar-item-container"> 
   <a href="./chapters/ch11_cluster.html" class="sidebar-item-text sidebar-link"><span class="chapter-title">11장 군집분석 개요</span></a>
   </div>
+</li>
+        <li class="sidebar-item">
+  <div class="sidebar-item-container"> 
+  <a href="./chapters/ch12_cluster.html" class="sidebar-item-text sidebar-link"><span class="chapter-title">12장 계층적 군집방법</span></a>
+  </div>
 </li>
     </ul>
     </div>

search.json

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     "title": "11장 군집분석 개요",
     "section": "(예 11.3)",
     "text": "(예 11.3)\n상관계수\n\nrow_cor <- cor(t(dat2), use = \"pairwise.complete.obs\", method = \"pearson\")\nrow_cor\n\n           [,1]      [,2]      [,3]      [,4]      [,5]      [,6]      [,7]\n [1,] 1.0000000 0.9347195 0.6828741 0.7600371 0.5771595 0.9673518 0.7144034\n [2,] 0.9347195 1.0000000 0.8979182 0.9413798 0.8297018 0.9942708 0.9164427\n [3,] 0.6828741 0.8979182 1.0000000 0.9937702 0.9907060 0.8457246 0.9990286\n [4,] 0.7600371 0.9413798 0.9937702 1.0000000 0.9693748 0.8999271 0.9977160\n [5,] 0.5771595 0.8297018 0.9907060 0.9693748 1.0000000 0.7652812 0.9837496\n [6,] 0.9673518 0.9942708 0.8457246 0.8999271 0.7652812 1.0000000 0.8684181\n [7,] 0.7144034 0.9164427 0.9990286 0.9977160 0.9837496 0.8684181 1.0000000\n [8,] 0.8099343 0.9655028 0.9815576 0.9967479 0.9464324 0.9321377 0.9890283\n [9,] 0.8219949 0.9707253 0.9773556 0.9948498 0.9394895 0.9394895 0.9857309\n[10,] 0.8089800 0.9650783 0.9818670 0.9968776 0.9469560 0.9315479 0.9892671\n           [,8]      [,9]     [,10]\n [1,] 0.8099343 0.8219949 0.8089800\n [2,] 0.9655028 0.9707253 0.9650783\n [3,] 0.9815576 0.9773556 0.9818670\n [4,] 0.9967479 0.9948498 0.9968776\n [5,] 0.9464324 0.9394895 0.9469560\n [6,] 0.9321377 0.9394895 0.9315479\n [7,] 0.9890283 0.9857309 0.9892671\n [8,] 1.0000000 0.9997823 0.9999987\n [9,] 0.9997823 1.0000000 0.9997471\n[10,] 0.9999987 0.9997471 1.0000000\n\n\n표준화한 후 상관계수\n\ns_dat2 <- scale(dat2)\nrow_cor_s <- cor(t(s_dat2), use = \"pairwise.complete.obs\", method = \"pearson\")\nrow_cor_s\n\n            [,1]       [,2]        [,3]        [,4]       [,5]        [,6]\n [1,]  1.0000000  0.9999315 -0.67406561  0.16745765  0.1015587  0.97213475\n [2,]  0.9999315  1.0000000 -0.66537519  0.17898338  0.1131937  0.96932487\n [3,] -0.6740656 -0.6653752  1.00000000  0.61536345  0.6663950 -0.82844400\n [4,]  0.1674577  0.1789834  0.61536345  1.00000000  0.9977886 -0.06832112\n [5,]  0.1015587  0.1131937  0.66639497  0.99778861  1.0000000 -0.13448190\n [6,]  0.9721348  0.9693249 -0.82844400 -0.06832112 -0.1344819  1.00000000\n [7,] -0.8797846 -0.8741613  0.94417672  0.32133338  0.3835649 -0.96670729\n [8,] -0.8006556 -0.8076120  0.09713788 -0.72474074 -0.6773407 -0.63789659\n [9,] -0.7482030 -0.7559160  0.01425163 -0.77939352 -0.7360260 -0.57182171\n[10,] -0.7956097 -0.8026447  0.08879897 -0.73048596 -0.6834783 -0.63142435\n            [,7]        [,8]        [,9]       [,10]\n [1,] -0.8797846 -0.80065556 -0.74820299 -0.79560974\n [2,] -0.8741613 -0.80761196 -0.75591598 -0.80264471\n [3,]  0.9441767  0.09713788  0.01425163  0.08879897\n [4,]  0.3213334 -0.72474074 -0.77939352 -0.73048596\n [5,]  0.3835649 -0.67734074 -0.73602598 -0.68347830\n [6,] -0.9667073 -0.63789659 -0.57182171 -0.63142435\n [7,]  1.0000000  0.41959679  0.34286201  0.41197990\n [8,]  0.4195968  1.00000000  0.99655423  0.99996493\n [9,]  0.3428620  0.99655423  1.00000000  0.99721394\n[10,]  0.4119799  0.99996493  0.99721394  1.00000000"
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+    "href": "chapters/ch12_cluster.html#예-12.1",
+    "title": "12장 계층적 군집방법",
+    "section": "(예 12.1)",
+    "text": "(예 12.1)\n데이터 읽기\n\ndat1 <- read.csv(\"data/ch12_dat1.csv\")\n\ndat2 <- dat1[, -1]\n\n유클리디안 거리\n\nD1 <- dist(dat2)\nD1 <- round(D1, 2)\nD1\n\n       1     2     3     4     5     6     7     8     9\n2   2.24                                                \n3  11.31  9.22                                          \n4   7.81  5.83  3.61                                    \n5  11.40  9.22  1.41  4.12                              \n6   1.41  2.24 11.40  8.06 11.31                        \n7  10.63  8.60  1.00  2.83  2.24 10.82                  \n8  10.05  9.49  9.22  7.21 10.44 11.18  8.25            \n9  11.40 11.18 11.40  9.43 12.65 12.65 10.44  2.24      \n10 12.37 12.08 11.70 10.00 13.00 13.60 10.77  2.83  1.00\n\n\n평균연결법\n\nhc_c <- hclust(D1, method = \"average\")\nplot(hc_c,\n  hang = -1, cex = 0.7, main = \"Average linkage with Euclidean distance\",\n  ylab = \"Distance\", xlab = \"observation\"\n)"
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+    "objectID": "chapters/ch12_cluster.html#예-12.2",
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+    "title": "12장 계층적 군집방법",
+    "section": "(예 12.2)",
+    "text": "(예 12.2)\n데이터 읽기\n\ndat1 <- read.csv(\"data/ch12_dat2.csv\")\n\ndat2 <- dat1[, -1]\n\n유클리디안 거리\n\nD1 <- dist(dat2)\nD1 <- round(D1, 2)\nD1\n\n      1     2     3     4     5     6     7\n2 16.12                                    \n3  2.24 17.00                              \n4 18.60  9.06 18.36                        \n5 13.15  5.00 14.56 13.15                  \n6  5.66 12.17  5.39 13.04 10.82            \n7 15.30  1.41 16.03  8.00  5.39 11.05      \n8 16.64  7.28 16.55  2.24 11.05 11.18  6.08\n\n\n워드 방법\n\nhc_c <- hclust(D1, method = \"ward.D2\")\nplot(hc_c,\n  hang = -1, cex = 1, main = \"Ward's linkage with Euclidean distance\",\n  ylab = \"Distance\", xlab = \"observation\"\n)"
+  },
+  {
+    "objectID": "chapters/ch12_cluster.html#예-12.3",
+    "href": "chapters/ch12_cluster.html#예-12.3",
+    "title": "12장 계층적 군집방법",
+    "section": "(예 12.3)",
+    "text": "(예 12.3)\n패키지 로드\n\nlibrary(cluster)\n\n데이터 읽기\n\ndat1 <- read.csv(\"data/ch12_dat3.csv\")\n\ndat2 <- dat1[, -1]\n\n다이아나 방법\n\nclus_diana <- diana(dat2)\n\nplot(clus_diana, which.plot = 2, main = \"Dendrogram of Diana\")"
+  },
+  {
+    "objectID": "chapters/ch12_cluster.html#예-12.4",
+    "href": "chapters/ch12_cluster.html#예-12.4",
+    "title": "12장 계층적 군집방법",
+    "section": "(예 12.4)",
+    "text": "(예 12.4)\n데이터 로드\n\ndat1 <- read.csv(\"data/ch12_dat2.csv\")\n\ndat2 <- dat1[, -1]\n\n패키지 로드\n\nlibrary(factoextra)\n\nLoading required package: ggplot2\n\n\nWelcome! Want to learn more? See two factoextra-related books at https://goo.gl/ve3WBa\n\n\n군집수 결정 - total within sum of square\n\nfviz_nbclust(dat2, kmeans, method = \"wss\", k.max = 5)\n\n\n\n\n군집수 결정 - gap statistics\n\nfviz_nbclust(dat2, kmeans, method = \"gap_stat\", k.max = 5)\n\n\n\n\n군집수 결정 - average silhouette width\n\nfviz_nbclust(dat2, kmeans, method = \"silhouette\", k.max = 5)"
   }
 ]