hierarchical-clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from scipy.cluster.hierarchy import linkage, dendrogram\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.DataFrame({\n",
    "    '身長': [160, 170, 175, 180],\n",
    "    '体重': [60, 65, 80, 75]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# データから距離行列を計算してlinkageを実行\n",
    "Z = linkage(data, method='ward')\n",
    "\n",
    "# デンドログラムの表示\n",
    "plt.figure(figsize=(10, 7))\n",
    "dendrogram(Z, labels=['人1', '人2', '人3', '人4'])\n",
    "plt.title('デンドログラム')\n",
    "plt.xlabel('個人')\n",
    "plt.ylabel('クラスタ間距離')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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