{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import AgglomerativeClustering\n",
        "from scipy.cluster.hierarchy import dendrogram\n",
        "from sklearn.datasets import make_blobs\n",
        "\n",
        "X, _ = make_blobs(n_samples=30, centers=3, cluster_std=10, random_state=42)\n",
        "\n",
        "clustering = AgglomerativeClustering(n_clusters=3)\n",
        "labels = clustering.fit_predict(X)\n",
        "\n",
        "agg = AgglomerativeClustering(distance_threshold=0, n_clusters=None)\n",
        "agg.fit(X)\n",
        "\n",
        "\n",
        "def plot_dendrogram(model, **kwargs):\n",
        "    counts = np.zeros(model.children_.shape[0])\n",
        "    n_samples = len(model.labels_)\n",
        "\n",
        "    for i, merge in enumerate(model.children_):\n",
        "        current_count = 0\n",
        "        for child_idx in merge:\n",
        "            if child_idx < n_samples:\n",
        "                current_count += 1\n",
        "            else:\n",
        "                current_count += counts[child_idx - n_samples]\n",
        "        counts[i] = current_count\n",
        "\n",
        "    linkage_matrix = np.column_stack(\n",
        "        [model.children_, model.distances_, counts]).astype(float)\n",
        "    dendrogram(linkage_matrix, **kwargs)\n",
        "\n",
        "\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 6))\n",
        "\n",
        "ax1.scatter(X[:, 0], X[:, 1], c=labels, cmap='viridis', s=70)\n",
        "ax1.set_title(\"Agglomerative Clustering\")\n",
        "ax1.set_xlabel(\"Feature 1\")\n",
        "ax1.set_ylabel(\"Feature 2\")\n",
        "\n",
        "plt.sca(ax2)\n",
        "plot_dendrogram(agg, truncate_mode='level', p=5)\n",
        "plt.title(\"Hierarchical Clustering Dendrogram\")\n",
        "plt.xlabel(\"Sample index\")\n",
        "plt.ylabel(\"Distance\")\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "id": "XlgHavjGdVKu",
        "outputId": "022f9ac4-d8fe-45c5-f84f-33db114e7cf0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1400x600 with 2 Axes>"
            ],
            "image/png": 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Vrkt8Ths2bJgsy3I9b926tYwxGjZsmKvNbrerVatW2rFjh6tt/vz5ioqK0lVXXaXDhw+7Hi1btlRERESekZO1a9dWQkJCnsxnzlt7emRKp06dtGPHDtdl/u6wLEv9+/fXZ599phMnTrja33vvPVWrVs11GdyyZcuUnJysgQMH5spvt9vVunXrc478TE1NVbly5dzO566oqChJ0tKlS/Od2qEwxhh9+OGH6tWrl4wxufYzISFBKSkpWr9+fa51hgwZUuS5hCMiIjRo0CDX86CgIF1++eW5PidLlixRtWrV1Lt3b1dbSEhIrt8DAAD82ZQpU7Rs2bI8j0suueSc686fP18dOnRQdHR0rr/T3bp1k8Ph0DfffJOrf79+/VS5cuVcbXa73XUljNPp1NGjR5WTk6NWrVrl+Tuf3zYOHTqkb775Rrfddptq1qyZq++Zx4in3Xnnnbmed+jQQUeOHMlzjHmmDz/8UM2aNdO1116bZ9np13B3P86lpI7VQkNDFRQUpBUrVuSZCqwoiuMzUJj8/n+dfaxWlHOUCxEREaHjx49LOr/j01tvvTXX1V4dOnSQJNd+/PTTTzp48KDuvPPOXP2GDh3qOpY+29nHvKe3cffdd+ea/7dnz55q0KCBPv30U0nnd65WHL+3/fv3z7Uvp6+WHDRokAICAnK1Z2Vl5Tt1A+DrmAYBQLFwOByaN2+eunTpop07d7raW7durRdeeEHLly9X9+7dJZ2at7NKlSoKCwvLtY169erler579+5820+3FXaweujQIaWlpbmmGDhTw4YN5XQ6tXfvXjVu3NjVfvZB+emDgLMvF4qKisp1ALp9+3alpKQoJiYm3ywHDx7M9bx27dr59vvuu+80btw4rV69Ok8xMiUlpcADrMLccMMNmjRpkhYtWqSbbrpJJ06c0Geffabhw4e7Tgi2b98uSQXemTYyMrLQ14iMjHQddHpS7dq1NXr0aL344ouaO3euOnTooN69e2vQoEHnfG8OHTqk5ORkzZgxQzNmzMi3T1H/P+WnevXqeU7ioqOj9fPPP7ue7969W3Xr1s3TL7/PNwAA/ujyyy9Xq1at8rSfLr4VZvv27fr5558LLL4V9e/0W2+9pRdeeEFbt27NNf1Cfv3Pbjtd8GrSpEmhWU87+9gxOjpa0qnpkQo6fvrzzz/Vr1+/c27bnf04l8jIyFxFSU8JDg7Ws88+q/vvv1+xsbFq06aNrrnmGg0ePFhxcXHnXL+4PgP5CQkJybPd6OjoXMf0RT1HuRAnTpxwFc7P5/i0sM+c9P/nT/Xr18/VLzAwMNcAljOd/T6e3kZ+51ENGjTQqlWrcvVz51ytOH5v3Tlnk3ReXxwA3kaxFkCx+Oqrr3TgwAHNmzdP8+bNy7N87ty5rmKtrzo9f2tR2s0ZNxhzOp2KiYkp8GZqZx8Y5jda888//9SVV16pBg0a6MUXX1SNGjUUFBSkzz77TC+99FKB82+dS5s2bVSrVi29//77uummm7R48WKlp6frhhtuyJVfOjVvbX4H0md+Q52fBg0aaOPGjcrKyjqvu7nmN1JFUp4bPkjSCy+8oKFDh+rjjz/WF198oXvvvVcTJ07UmjVrVL169QJf4/Q+Dho0qMB5x84e9VPUUbVSwZ+dMz8nAACgYE6nU1dddZUeeuihfJdfdNFFuZ7n93f67bff1tChQ9W3b189+OCDiomJkd1u18SJE/Xnn3/m6e/O3/r8eOrvv7v7cS4NGjTQhg0btHfv3nznLD0Xd47V7rvvPvXq1UsfffSRli5dqscff1wTJ07UV199pebNmxf6OsXxGShIQf+vSlJ2drZ+//1315cB53N86onP3IX+Hlzoa7n7eXfnnE3ieBz+iWItgGIxd+5cxcTEaMqUKXmWLViwQAsXLtT06dMVGhqq+Ph4ff3110pLS8v1zfXZdzKNj4/Pt72gtjNVrlxZYWFh2rZtW55lW7dulc1mO6+D1fzUrVtXX375pdq1a3feBzuLFy9WZmamFi1alOvb4vymICjogLkgAwYM0Msvv6zU1FS99957qlWrltq0aZMrvyTFxMSoW7dubmfv1auXVq9erQ8//FADBw50e/3TIwKSk5NztZ/+tv5sTZs2VdOmTfXYY4/p+++/V7t27TR9+nQ9/fTTkvJ/fypXrqxy5crJ4XCc1z4Wh/j4eG3ZskXGmFwZz/VZBgCgLKhbt65OnDhxQX+nP/jgA9WpU0cLFizI9bd23LhxRVr/9MjDX3755bwznEvdunXPuf0L3Y+z9erVS++++67efvttjRkzxu313T1Wq1u3ru6//37df//92r59uy699FK98MILevvttyUVfCxbHJ+BC1HUc5Tz9cEHHyg9Pd01HZonjk9Pnz9t374911Vz2dnZ2rlzp5o1a1bkbWzbti3PlXfbtm1zLb+Qc7UzFffnHSgNmLMWwAVLT0/XggULdM011+j666/P87jnnnt0/Phx151oExISlJ2drddff921DafTmafQW7VqVTVp0kRz5szJNefqypUrtXnz5kIz2e12de/eXR9//LF27drlak9KStI777yj9u3bn/Py/qIaMGCAHA6HnnrqqTzLcnJy8hzYFpRXyv3Nb0pKimbNmpWnb3h4eJG2edoNN9ygzMxMvfXWW1qyZIkGDBiQa3lCQoIiIyM1YcKEfO/WfOjQoUK3f+edd6pKlSq6//779fvvv+dZfvDgQVchNT+ni8VnzkPmcDjyXA6WmpqqnJycXG1NmzaVzWZTZmamqy2/98dut6tfv3768MMP8z1BOtc+FoeEhATt27fP9XsgSRkZGbl+DwAAKKsGDBig1atXa+nSpXmWJScn5zkGyE9+x1M//PCDVq9eXaQMlStXVseOHfXmm29qz549uZYV1+i8fv36adOmTVq4cGGeZadf40L342zXX3+9mjZtqvHjx+e7jePHj+vRRx8tcP34+HjZ7fY8c8ZOnTo11/O0tDRlZGTkaqtbt67KlSt3zmM1qXg+AxeiqOco52PTpk267777FB0d7ZoD1xPHp61atVLlypU1ffp0ZWVludpnz55d5POHVq1aKSYmRtOnT8/1/+3zzz/Xb7/9pp49e0q6sHO1MxX35x0oDRhZC+CCLVq0SMePH89146QztWnTRpUrV9bcuXN1ww03qG/fvrr88st1//33648//lCDBg20aNEiHT16VFLub9snTJigPn36qF27drr11lt17NgxTZ48WU2aNMl1UJCfp59+WsuWLVP79u119913KyAgQK+99poyMzP13HPPFdv+d+rUScOHD9fEiRO1ceNGde/eXYGBgdq+fbvmz5+vl19+Wddff32h2+jevbuCgoLUq1cvDR8+XCdOnNDrr7+umJgYHThwIFffli1batq0aXr66adVr149xcTEFDjfrCS1aNFC9erV06OPPqrMzMxcUyBIp+YxmzZtmm655Ra1aNFCN954oypXrqw9e/bo008/Vbt27TR58uQCtx8dHa2FCxfq6quv1qWXXqpBgwapZcuWkqT169fr3XffVdu2bQtcv3HjxmrTpo3GjBmjo0ePqkKFCpo3b16eA/KvvvpK99xzj/r376+LLrpIOTk5+t///uc60D3z/fnyyy/14osvqmrVqqpdu7Zat26tZ555Rl9//bVat26t22+/XY0aNdLRo0e1fv16ffnll67Pn6cMHz5ckydP1sCBA/Wvf/1LVapU0dy5c103bnB3xDQAAKXJgw8+qEWLFumaa67R0KFD1bJlS508eVKbN2/WBx98oF27dqlSpUqFbuOaa67RggULdO2116pnz57auXOnpk+frkaNGp3zuPG0V155Re3bt1eLFi10xx13qHbt2tq1a5c+/fRTbdy4sVj284MPPlD//v112223qWXLljp69KgWLVqk6dOnq1mzZsWyH2cKDAzUggUL1K1bN3Xs2FEDBgxQu3btFBgYqF9//VXvvPOOoqOjNX78+HzXj4qKUv/+/fXqq6/KsizVrVtXn3zySZ75VH///XddeeWVGjBggBo1aqSAgAAtXLhQSUlJuvHGG139CjqWLY7PwIVw5xylMN9++60yMjLkcDh05MgRfffdd1q0aJGioqK0cOHCXNOOFffxaWBgoJ5++mkNHz5cXbt21Q033KCdO3dq1qxZBc5Zm982nn32Wd16663q1KmTBg4cqKSkJL388suqVauWRo0a5ep7IedqpxX35x0oFQwAXKBevXqZkJAQc/LkyQL7DB061AQGBprDhw8bY4w5dOiQuemmm0y5cuVMVFSUGTp0qPnuu++MJDNv3rxc686bN880aNDABAcHmyZNmphFixaZfv36mQYNGuTqJ8mMGzcuV9v69etNQkKCiYiIMGFhYaZLly7m+++/z9Vn1qxZRpJZu3ZtrvZx48YZSebQoUO52ocMGWLCw8Pz7OOMGTNMy5YtTWhoqClXrpxp2rSpeeihh8z+/ftdfeLj403Pnj3zfY8WLVpkLrnkEhMSEmJq1aplnn32WfPmm28aSWbnzp2ufomJiaZnz56mXLlyRpLp1KmTMcaYr7/+2kgyX3/9dZ5tP/roo0aSqVevXr6vfXr9hIQEExUVZUJCQkzdunXN0KFDzU8//VTgOmfav3+/GTVqlLnoootMSEiICQsLMy1btjTjx483KSkprn6dOnVyZT7tzz//NN26dTPBwcEmNjbW/Pvf/zbLli3LtT87duwwt912m6lbt64JCQkxFSpUMF26dDFffvllrm1t3brVdOzY0YSGhhpJZsiQIa5lSUlJZsSIEaZGjRomMDDQxMXFmSuvvNLMmDEj1/sgycyfPz/f9+js97hTp06mcePGefoOGTLExMfH52rbsWOH6dmzpwkNDTWVK1c2999/v/nwww+NJLNmzZpzvMMAAPimgo6lTsvvb2V8fHyuv9HGGHP8+HEzZswYU69ePRMUFGQqVapkrrjiCvP888+brKwsY4wxO3fuNJLMf//73zyv43Q6zYQJE0x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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.datasets import make_blobs\n",
        "from scipy.cluster.hierarchy import dendrogram, linkage\n",
        "\n",
        "X, _ = make_blobs(n_samples=30, centers=5, cluster_std=10, random_state=42)\n",
        "\n",
        "\n",
        "def divisive_clustering(data, max_clusters=3):\n",
        "    clusters = [data]\n",
        "    while len(clusters) < max_clusters:\n",
        "        cluster_to_split = max(clusters, key=lambda x: len(x))\n",
        "        clusters.remove(cluster_to_split)\n",
        "\n",
        "        kmeans = KMeans(n_clusters=2, random_state=42).fit(cluster_to_split)\n",
        "        cluster1 = cluster_to_split[kmeans.labels_ == 0]\n",
        "        cluster2 = cluster_to_split[kmeans.labels_ == 1]\n",
        "\n",
        "        clusters.extend([cluster1, cluster2])\n",
        "    return clusters\n",
        "\n",
        "\n",
        "clusters = divisive_clustering(X, max_clusters=3)\n",
        "\n",
        "plt.figure(figsize=(12, 5))\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "colors = ['r', 'g', 'b', 'c', 'm', 'y']\n",
        "for i, cluster in enumerate(clusters):\n",
        "    plt.scatter(cluster[:, 0], cluster[:, 1], s=50,\n",
        "                c=colors[i], label=f'Cluster {i+1}')\n",
        "plt.title('Divisive Clustering Result')\n",
        "plt.legend()\n",
        "\n",
        "linked = linkage(X, method='ward')\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "dendrogram(linked, orientation='top',\n",
        "           distance_sort='descending', show_leaf_counts=True)\n",
        "plt.title('Hierarchical Clustering Dendrogram')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "CdZCvz7pecka",
        "outputId": "91084bb8-6dae-4b03-c658-08b53f03e46e"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "zXcphR2Wef3Q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}