Kmeans_iris.ipynb

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      "name": "Kmeans_iris.ipynb",
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      "cell_type": "markdown",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/imji0319/PDSH/blob/master/Kmeans_iris.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
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        "id": "lMF4J7lufbPJ",
        "colab_type": "code",
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      },
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import scale\n",
        "from sklearn.datasets import load_iris\n",
        "from sklearn.cluster import KMeans\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd \n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
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      },
      "cell_type": "code",
      "source": [
        "iris = load_iris()\n",
        "irisDF = pd.DataFrame(data = iris.data , columns = ['sepal_length', 'sepal_width' ,'petal_length', 'petal_width'])\n",
        "irisDF.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width\n",
              "0           5.1          3.5           1.4          0.2\n",
              "1           4.9          3.0           1.4          0.2\n",
              "2           4.7          3.2           1.3          0.2"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 2
        }
      ]
    },
    {
      "metadata": {
        "id": "4fjA1Xrpg7pD",
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      },
      "cell_type": "code",
      "source": [
        "# 3개 그룹으로 군집화 \n",
        "# 초기 중심 설정 방식 : k-means++\n",
        "# 최대 반복 횟수 : max_iter = 300\n",
        "kmeans = KMeans(n_clusters=3, init='k-means++', max_iter=300, random_state =0).fit(irisDF)"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "colab": {
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      },
      "cell_type": "code",
      "source": [
        "print(kmeans.labels_)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 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 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
            " 0 0 0 2 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 2 0 2 2 2 2 0 2 2 2 2\n",
            " 2 2 0 0 2 2 2 2 0 2 0 2 0 2 2 0 0 2 2 2 2 2 0 2 2 2 2 0 2 2 2 0 2 2 2 0 2\n",
            " 2 0]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
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        "id": "SxPClAwKhtx6",
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      },
      "cell_type": "code",
      "source": [
        "irisDF['cluster']=kmeans.labels_"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "V_MaoOQCh5g2",
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        "outputId": "55506a71-0344-46ec-b208-7553709c89f9",
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        }
      },
      "cell_type": "code",
      "source": [
        "irisDF.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>cluster</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width  cluster\n",
              "0           5.1          3.5           1.4          0.2        1\n",
              "1           4.9          3.0           1.4          0.2        1\n",
              "2           4.7          3.2           1.3          0.2        1"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "CSe4fLzih8FG",
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        "outputId": "f84d4448-ad93-463e-f7b9-0249bdb521aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        }
      },
      "cell_type": "code",
      "source": [
        "irisDF['target']=iris.target\n",
        "iris_result = irisDF.groupby(['target','cluster'])['sepal_length'].count()\n",
        "print(iris_result)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "target  cluster\n",
            "0       1          50\n",
            "1       0          48\n",
            "        2           2\n",
            "2       0          14\n",
            "        2          36\n",
            "Name: sepal_length, dtype: int64\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "vO-xrT8wiONb",
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      },
      "cell_type": "code",
      "source": [
        "# PCA 를 이용해 4개의 속성을 2개로 차원 축소하여 데이텨 표현 \n",
        "\n",
        "from sklearn.decomposition import PCA\n",
        "\n",
        "pca = PCA(n_components=2)\n",
        "pca_transformed = pca.fit_transform(iris.data)\n",
        "\n",
        "irisDF['pca_x'] = pca_transformed[:, 0]\n",
        "irisDF['pca_y'] = pca_transformed[:, 1 ]\n",
        "irisDF.head(3)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>cluster</th>\n",
              "      <th>target</th>\n",
              "      <th>pca_x</th>\n",
              "      <th>pca_y</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
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              "      <td>-2.684126</td>\n",
              "      <td>0.319397</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.714142</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
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              "      <td>-2.888991</td>\n",
              "      <td>-0.144949</td>\n",
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            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width  cluster  target  \\\n",
              "0           5.1          3.5           1.4          0.2        1       0   \n",
              "1           4.9          3.0           1.4          0.2        1       0   \n",
              "2           4.7          3.2           1.3          0.2        1       0   \n",
              "\n",
              "      pca_x     pca_y  \n",
              "0 -2.684126  0.319397  \n",
              "1 -2.714142 -0.177001  \n",
              "2 -2.888991 -0.144949  "
            ]
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          "execution_count": 8
        }
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      },
      "cell_type": "code",
      "source": [
        "# 산점도\n",
        "\n",
        "marker0_ind = irisDF[irisDF['cluster']==0].index\n",
        "marker1_ind  = irisDF[irisDF['cluster']==1].index\n",
        "marker2_ind = irisDF[irisDF['cluster']==2].index\n",
        "\n",
        "\n",
        "plt.scatter(x=irisDF.loc[marker0_ind, 'pca_x'], y=irisDF.loc[marker0_ind, 'pca_y'], marker='o')\n",
        "plt.scatter(x=irisDF.loc[marker1_ind,  'pca_x'], y=irisDF.loc[marker1_ind,  'pca_y'], marker='s')\n",
        "plt.scatter(x=irisDF.loc[marker2_ind, 'pca_x'], y=irisDF.loc[marker2_ind,  'pca_y'], marker='^')\n",
        "\n",
        "plt.xlabel('PCA 1')\n",
        "plt.ylabel('PCA 2')\n",
        "plt.title('3 cluster visualization by 2 PCA Components')\n",
        "plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HjUzM7BQzGzSzweHh4bKLIyLSs6oeNIaAHSOPdwifG8PdL3T3AXcfmD59escKJyIy3lQ9\naFwHnBD2otofeMHdnyq7UCIi41WpvafM7HLg3cA0M1sD/C0wEcDdFwE3AIcBq4B1wJ+UU1IREYGS\ng4a7H99guwN/1qHiiIhIA1VvnhIRkQope3CftOrc/vip1CdN0eBBESmMahrdKmntDa3JISIFUtAQ\nEZHMFDRERCQz5TQ6RTkIEekBqml0SloO4lxNpyUi3UFBowpaSV5rTQ4RKYGap7qVmrREOmp43TAn\n3HgClx52KdMmTyu7OKVRTUNEJINFP1nE0MtDLLp3UdlFKZWChohIA8Prhrl21bU4zpJVS/jl+l+W\nXaTSqHmqUyZNaW/gnXpfiZRm0U8WMeIjAIz4CIvuXcRZ+59VcqnKoZpGp5w51F7yWiPARUpRq2Vs\nGNkAwIaRDeO6tqGaRiepRiDSdaK1jJrxXNtQTUNEJMUtq28ZrWXUbBjZwM2rby6pROVSTUNERi25\ne4jzlj7Ek2vXM2PqZE4/ZDbz5ozvwafLjl7WcJ/x1B1XNQ0RAYKAccY1Kxlaux4Hhtau54xrVrLk\nbjWrNjKeuuMqaHQLjQCXgp239CHWb9g05rn1GzZx3tKHSipRdxhv3XHVPFW2rF1plUSXgj25dn1T\nz0tgvHXHVU2jbOpKKxUxY+rkpp6X8dkdV0FDRAA4/ZDZTJ7YN+a5yRP7OP2Q2SWVqPrSuuP2KjVP\ndVJSU5RIBdR6San3VHZp3XF7tYlKQaOTFDCk4ubN6VeQaEKW7ri9Rs1TIiKSmYJGVakrrUhhhtcN\nM/fquT2dsC6KgkZVqYutSGGqMhivG4OXgoaIjCtVGoxXleDVDAWNTlKTk0jp4gbjlaFKwasZpQYN\nMzvUzB4ys1VmNj9m+0lmNmxm94S3k8soZ27OHIIFL5RdCpG2dGOTSk2VBuNVJXg1q7SgYWZ9wFeA\nucAewPFmtkfMrle6+z7h7WsdLaSIbKYbm1RqqjIYr0rBq1ll1jT2A1a5+6Pu/ipwBXBEieURkQa6\ntUmlpiprY1QleLWizMF9/cDqyOM1wDti9jvKzH4PeBj4tLuvrt/BzE4BTgGYOXNmAUUV6R5FronR\nDZPzpa1tUZXBeEnB66YnbuL2odsrvS5H1RPh3wFmuftewA+Ab8Tt5O4XuvuAuw9Mnz69owUUqZIi\n18ToliaVbmg+W3b0MlaeuHKz20EzD6p82csMGkPAjpHHO4TPjXL3Z939lfDh14B9O1S2YrXbi+rc\nfliwzea3czX9w3hX5JoY3dCk0s3NZ91S9jKDxl3Abma2s5lNAo4DrovuYGbbRx4eDjzQwfIVp91e\nVJpOXRIUuSZGVfIBabq1RxJ0T9lLy2m4+0Yz+ySwFOgDLnb3+83s74BBd78O+AszOxzYCDwHnFRW\neTtuwTabP1e/MJNInRlTJzMUEyDS1sTImgMpKh+Q1/raSc1np+59amXzAzXdVPZScxrufoO77+7u\nu7j7F8PnPh8GDNz9DHd/i7vv7e7vcfcHyyxv6V59OT6YiISaXROjCuuC55WD6IbmsyTdVPaqJ8JF\npAnz5vTzpSP3pH/qZAzonzqZLx25Z2LvqbLXBc+zHb+I5rNODWTshqa/Gq2nURYlraUgzayJUfa6\n4Fm68GZtvoo2n519x9lc9dBVHDP7mLa6BEdrQUV2La5KV+AsVNMoSxFJa81tJU0qc13wrF14m22+\nyqv2Enecbp5CJS8KGt1uwQu/uSlJLk0qc13wLO34jQJA3EU8Sy+kLBf/uON0wxiQoiloFKnV8RSq\nMUiLltw9xAELl7Pz/Os5YOHyhgntZnMgecrSjt8oANRfxPOqvcQdZ/GqxSz52ZLKj6Momrl72WXI\n1cDAgA8ODpZdjECrPZ3qx3Cc2x/fnKUuuBJR6wkVTWwb8KH9Z3LOvD3LK1iLhtcNM/eaubyy6ZXR\n57bs25LvHfU9pk2eNmZ77fmv3vtVFv9s8ZhgNHHCRI7c7cjRnETc6+pzJWffcfZmx5nABDz8r/6Y\nvcDMVrj7QKP9VNPoBrXBgPU3BQyJiOsJ5cBldzzR0S60eWnUfBVXC8mj9gLxtaARRnB89Jjjtbah\noFE1apqSFiX1eHIY7ULbbPNVmdICQFIz1JXvv5KVJ67kmNnHYBjHzj6WlSeuHO2dlLX5qn5uqGNm\nH8PECRPH7FPVcRRFU5fbvCU1JTWixZmkTUmjwSEIKPXNV7WBfEBbOYy8RnTXS+uGevYdZyfWQj6+\n18fHJM+jo6rP//H5Y5q7oq9La2pKC2C91ESVhWoaedP8T1KS0w+ZjSVsmzF1cmED+croUZR2EU9r\nfvrBYz/Y7FhZBtElzUpbhfEVne4GrJqGSI+YN6efwcef47I7niDavaXWhfbTV94T+7q4Zq2s81HV\nd4k9de9TcfdCah5RSRfrWpI7bg4ndx/NSSQlwLtRpwYg1qimURWa2lxycM68PTn/2H0260ILMMHi\n6yH1A/mamY+qamMZ0pLn3TKLbDPKmE5dQaPT0nIXatqSHMyb08/t8w/k5wvfx+3zDwTgjGtWsimm\ne33cQL6szVixYxl+VtxYhizNMEnNVsueWNYVC0g1q4xAqKAh0uPiggBAn1nsQL6s81HF/arfMLKB\njSMbgfwvYllqMEm5hwNnHtg1s8hmVdZKiqlBw8y2NrNdYp7fq7gidbk8usxqZT7JKEsX2qQgMOIe\nm6fIOh9V3K96xxkhuDg3cxFrVItotxmmm2aRzaqs6dQTg4aZHQM8CFxtZveb2dsjm79eaKm62ZlD\nyYEja0DRynySQdbcQ7OTEmadjyrPsQyNahFpzTBZmq2q3PupVWUFwrTeU2cC+7r7U2a2H3CpmZ3h\n7oshsWefQPJI7VoNQiQHabmHaA3i9ENmbza9SNqkhLXXZuk9FdXqWIa4HljRXk2NVrXrdO+hqigr\n4KUFjT53fwrA3X9kZu8BvmtmOwK9NWFVpzSqKWg0uDQQ7Qqb9CWsb45qJQg0syZHTasXsUZraqQ1\nw6QN5JNipAWNl8xsF3d/BCCscbwbWAK8pROF66iyJwWcNEXLuUqquAkJ48Q1O7USBDohy9rYaTUY\nxxsu4iT5SkuEf4K6Zih3fwk4FPhIkYUqRdl5BOUrpIGkXlBRnVoLIy8L71yYOK1HzbKjl202l9TK\nE1dyxfuu6MlutFWXGDTc/V53XxXz/AZ3v6zYYvWQaE+odqn5alxLW4K102th5GXZ6s2btOqTuUk9\np8rqPTTeaRqRouVRg6g1XdU3X2k9jXElaULC/qmTRwfxdZMHn32QTf6bmtPVf3g1u2+7+2b7JeU8\nNIlgORQ0qqAWFJKU3XQmldBsL6gqiZsJd/5t88fs89lbP8uSeUs2e11SzqObu8t2s6aDRth76jh3\nP6+A8nSntCR6muiUIkqASwOtdoWNyjoRYd7qu8U++OyDPPLCI2P2eeSFR3j4uYfH1DbSmqBUmyhH\npmlEzGy6mf2pmd0G3AJsV2ipytDOgLw8agLtDgiUcaF+XqlmA0bWiQjzFJeTqK9l1Hz21s+OedyL\nI7m7XWJNw8xeBxwJfBDYHbgG2Nndd+hQ2Tqr7NxA2vlVC5EcZB0MmLe4nMTPX/x57L6PvvDomMdq\ngqqetOapZ4AfAWcB/+vubmYf6EyxekhSvkI1COmwrBMR5ikpJ7Hs6GW4O3Ovmcsrm16hz/rY5Js4\nZvYxhZVF8pEWNM4AjgP+HbjczK7sTJF6TH0Nopb/aKYnlALPuJZXHiKp91XSHFR5SMtJRAfm1XpR\naVR39SUGDXe/ALjAzH6LIHgsAWaY2V8Di9394XZPbmaHAv8C9AFfc/eFddu3BC4B9gWeBY5198fa\nPW+pWsl/lN10JqXJc13vMnpfpa1v8dKrL222bdPIJiW5K65hItzdH3X3c919T2AA2Bq4od0Tm1kf\n8BVgLrAHcLyZ7VG320eB5919V+B84O/bPW8hlMSWguS5rve8Of186cg9N1vVr8h8RjPrWwBs9I2F\njeru9FravSotEb4rsJ273157zt3vM7Mbgf/K4dz7Aavc/dHwfFcARwA/jexzBLAgvP/fwJfNzNxj\nliArk2oCUpC88xBVmYMqrgZSU1SX2vE6G27e0moaFwAvxjz/AsGv/nb1A6sjj9eEz8Xu4+4bw3O/\nof5AZnaKmQ2a2eDw8HAORasILcY07jWzFkaWBZmqolYDeeNr3rjZtla71KbVJMpYS7tXpSXCt3P3\nlfVPuvtKM5tVWIla4O4XAhcCDAwMVKsW0g6NBB/3suYh8sx9dFKeXWrTahKNpl+X7NJqGlNTtuXR\n3WII2DHyeIfwudh9zGwLYBuChHj3Uv5jXGu2NpA1D5Fn7qMbpdUkylpLu1el1TQGzexj7v6f0SfN\n7GRgRQ7nvgvYzcx2JggOxxEMJIy6DjgR+CHwR8DyyuUzmqX8x7jVam0gSx6imdxHWVOJFCmtJqGp\nSPKVFjROAxab2Yf4TZAYACYBbQ/yc/eNZvZJYClBl9uL3f1+M/s7YNDdrwMuIlhmdhXwHEFgqa6y\nF3KSSityRHbWMRjd2oyVptFCTpoNN19p4zR+AfxOuMzrW8Onr3f35Xmd3N1voK77rrt/PnL/18DR\neZ2vcGk5iLipQBRMxpVGtYF2agBZcx9lTSVSpEY1CU1Fkq+0LrdbAacCuwIrgYvCHkySl1owSQoe\nGgneU9JqA+3WAOJmwH3Pb0/nvKUP8ekr7xkNQkUGrrKoJtFZlpQiCKcN2QDcRjAA7zF3P62DZWvJ\nwMCADw4OlnPydiYWjE6TLj0pbo3vyRP7+NKRe3Le0odyXWAp6VxbTZzA8+s2Hx/RHwaIpPJVPXBI\n+8xshbsPNNovLaexRzgKHDO7iGDyQqmXlMdolpqvel7aehifvvKe2Ne0OogvqRlqyy0mMHliX2wz\nVqOmq26shUj+0oLG6M+RMGndgeJ0oSLHTGg8Rs9J6gnV6mSCSRfypGDzwvoNnH/sPqM1mz6z0cAQ\nd34IAlcvJtClNWnjNPY2sxfD20vAXrX7ZhY3UlxEWnT6IbOZPLFvzHONJhNMWlTprCUrmZDwI2/G\n1MnMm9M/er5NYfN0UsCovaYT40A0N1R3SAwa7t7n7luHt9e5+xaR+1t3spBdS3kKyaiVyQSTLuSX\n3fHEaDCIigahuNfGmdhnmRLoeYiO6JbqanqNcBEpRrOTCSZdsOO6tvSZjQlCWS/2r50UXCImmMUG\norzW4qgf0R1dU2N43TAn3HgClx52qdbZqIBMa4SLSPvynlCwmQv2iPuYgJT1tWvXb+CMa1Y2rLlk\nkdb8FDeiO7pt6OUhzl9xvpqvKkBBo11pYybaXdtb4zF6RlL+oZ3AEZcHSequUh8k4l4bp5Yoj3u+\n2a64Sc1PaXNDRWsg333ku2q+qgAFjXadOZRv7mLBC7+5qbttzygikRyXB/nQ/jMzJdTrX/v610xk\n4gTb7HVxNQzYvObSSNqEgmkjusfUQBhJnNpcSfTOUU5DJEbeYxKKSiTH5UEGdto2U9nrXxv3npO6\n4jaby0ibUDBpRPdNT9zEy6++vPmSsL75krBaYKlzEkeEd6vSRoS32xQ1ehz1uCpb2sjtZgNH7UKc\n1KW11RHfnZLH32J43TBzr5nLK5teGX1uy74t+d5R30tNbJ99x9ks/tni2BX+oq+PHj/LcSVe1hHh\nap6qEuUwKiGvpqRoHiNOs4nkMsyb089R+/bTF4776DPjqH2b6+WV1vyUJsuSsPXHz3JcaY+CRpmi\n+QvlMCojr6aktLEQ0ZHYVV6WdcndQ1y9Ymg0t7HJnatXDDVV5rQJBdNkWRJWCyx1nnIaeUmakVa6\nTl5TeqSNso6OxK7ydBx5TKXe7tTkaa8/+46ztcBSh6mmkZdaL6rarRE1RVVWXlN6ZFW1ZVmj40nS\n5qOqglZrMdI61TSakdfKfEp2V1rabLRJsk7LkaQqF+G4xHecvEaCt0sLLHWegkYz0lbmk56S15Qe\nWaVdhDs5JXmW4NcNCXwpjoKGjGt5XZAb5TDSpF2EG01J3qnxJBCMNtc6GqKgIeNWnmtExK16l0V/\ng4two+6/ea9xkRT84saTaFGm8UmJ8KIkJbqVAK+MPKf2yDItRz0Dbp9/YOqFNq37bxFTk2TtBFDE\nXFrSHVTTKIrGXFRe3lN71OdBzlqyksvvXJ04f1OWZHJa998ipibJ2gkgj6640p1U02iGag89Jemi\nnUfPoPpBcfWyJpNPP2Q2E/twm5NJAAAMt0lEQVTG1lhqCyMVVf55c/q5ff6B/Hzh+0bnn6qfzr0T\nizJJNamm0QzVHnpKXB4ir55Bab2QGuUxNlMfd8LHRZYf0nM+rQ6AlO6nmoaMW60ssZpV0i/uLHmM\nqPOWPsSGkbFRY8OIjzYDFVX+2rmTmqBaGQApvUE1DRnXmh2PkVVev8QbNQMVVf5G525lAKT0BgUN\nkQLk1XRUZjNQo3MXGbCkuhQ0mpXXVCLS09r9JR5dh8MYm9boVDNQ0TkT6U6lBA0z2xa4EpgFPAYc\n4+7Px+y3CVgZPnzC3Q/vVBkTaSoRySjul3iWAXH1CWiH0cDRdBK9zfJDfk1Qw+uGOeHGE7j0sEu1\nSFIXK6umMR9Y5u4LzWx++PivY/Zb7+77dKREqkFIwbKOQI9LQNcCRqdX+cuzCUpLsvaGsnpPHQF8\nI7z/DWBeSeX4DdUgpGBZR3D34hiI2mJJjmuRpC5XVk1jO3d/Krz/NLBdwn5bmdkgsBFY6O5L4nYy\ns1OAUwBmzpyZd1lFWhZtjoof5rd5MMgj+d3JeaGynCtuSVbVNrpTYTUNM7vJzO6LuR0R3c/dnc2H\nL9XsFC50/kHgAjPbJW4nd7/Q3QfcfWD69On5vhGRFtXPz5SkPhi0Owaik/NCZTmXlmTtLYXVNNz9\n4KRtZvYLM9ve3Z8ys+2BZxKOMRT+/1EzuwWYAzxSRHkzS1vWdcE2Y/dTLmRca3VtinYT0EXMCxWd\nR6vPjOPfsSPnzNsz07mitYwa1Ta6V1nNU9cBJwILw/9fW7+Dmb0eWOfur5jZNOAA4B86Wso49YEg\nGiiilAvpeY2aZdpZm6KdBHTeOZGzlqzkm3c8Mfp4k/vo4yznSluSVUGj+5QVNBYC3zazjwKPA8cA\nmNkAcKq7nwy8GfgPMxshaEZb6O4/LaxESTUITUYoMbL0hGpmbYo8NZsTaRT8Lr9zdezrLr9zdaZz\naUnW3lJK0HD3Z4GDYp4fBE4O7/8fsGfHCqWmJGlClmaZsgbHNXPeLMEvaabeTe4aADgOacJCkRZk\naZYpekLBJM2cN0s34D6LX0yqz6y09yjl0TQiIi3I2gRU1vxMWc+bJfgd/44dx+Q0os83cy7pDapp\ntEsLM41LvTI1eJaFnM6Ztycf3n/maI2jz4wP7z+Tc+Z1rvVYqsM8ob2yWw0MDPjg4GDZxZBxIK8B\ndJ0ciBd37richJqYxh8zWxGOi0ul5imROlkv4q1OSFi/f5b5qIqidTGkWappiES088u7ldcesHB5\nKd1yReqppiHSgqTeRF/4zv0Nf423MhK7FycnlN6mRLhIRNLF+vl1G8bMr/TpK+9h1vzrOWDh8tF5\nlloJAFkS0SJVoqAhEpH1Yl1r1I1O0NdKAOiVXlgyfihoSCUtuXuIAxYuZ+e6X/NFi7uIN1Jrgmol\nAGhwnHQb5TSkcsrsURTXm+hXr2xk7foNqa97cu36lnsiVW1wXJldgKX61HtKKqdqPYriekXV65Xe\nTknvderkiSw4/C0KHj1Mvaeka1WtR1G0BjG0dj3G2FXDeikHkbQGyNr1Gzo6fkSqSzkNqZwq9iia\nN6ef2+cfyGML38f5x+7TszmItMAct565jD+qaUjlVH267arlIPKUNBFjjcaPiGoaUjnd3qOorJ5f\neWjUe0zjR0Q1Damkbv01X/ZcUu2qlfEL37mf59eN7TFWpdqelEc1DZEcZVnUqOrmzenn7s//ARf0\ncO5GWqeahkiOqtbzqx3dWtuTYqmmIZKjKvb8EsmTgoZIjjSXlPQ6NU+J5EiLGkmvU9AQyZlyAdLL\nFDTycm4/vPry5s9PmgJndk8/fRGRNMpp5CUuYKQ9LyLShRQ0REQkMwUNERHJrJSgYWZHm9n9ZjZi\nZonzt5vZoWb2kJmtMrP5nSyjiIhsrqxE+H3AkcB/JO1gZn3AV4D3AmuAu8zsOnf/aWeKKFVVW1lu\naO16+szY5E6/uraKdEQpQcPdHwAws7Td9gNWufuj4b5XAEcA1Qwak6Yk956S3NRPCLgpXHmy2yYG\nFOlWVe5y2w+sjjxeA7wjbkczOwU4BWDmzJnFlyyOutV2RNLKcvCbiQEVNESKU1jQMLObgDfFbPqc\nu1+b57nc/ULgQgjWCM/z2FItjSb+68aJAUW6SWFBw90PbvMQQ8COkcc7hM/JONZoZTlNDChSrCp3\nub0L2M3MdjazScBxwHUll0lKlraynCYGFCleWV1uP2Bma4B3Ateb2dLw+RlmdgOAu28EPgksBR4A\nvu3u95dRXqmO6FKwAH1hZwotEiTSGebeWymAgYEBHxwcLLsYIiJdxcxWuHviuLmaKjdPiYhIxSho\niIhIZgoaIiKSmYKGiIhkpqAhIiKZKWiIiEhmVZ57SmRcqc3e++Ta9czQrL1SUQoaIhVQP3uvZu2V\nqlLzlEgFxM3eW5u1V6RKFDREKiBpdl7N2itVo6AhUgFJs/Nq1l6pGgUN6VlL7h7igIXL2Xn+9Ryw\ncDlL7q7uzPpxs/dq1l6pIiXCpSd1W2K5Vib1npKqU9CQnpSWWK7qhXjenP7Klk2kRs1T0pOUWBYp\nhoKG9CQllkWKoaAhPUmJZZFiKKchPUmJZZFiKGhIz1JiWSR/ap4SEZHMFDRERCQzBQ0REclMQUNE\nRDJT0BARkcwUNEREJDNz97LLkCszGwYe78CppgG/7MB58qCyFkNlLYbKWoxGZd3J3ac3OkjPBY1O\nMbNBdx8ouxxZqKzFUFmLobIWI6+yqnlKREQyU9AQEZHMFDRad2HZBWiCyloMlbUYKmsxcimrchoi\nIpKZahoiIpKZgoaIiGSmoNEGMzvbzH5iZveY2ffNbEbZZUpiZueZ2YNheReb2dSyy5TEzI42s/vN\nbMTMKtmd0cwONbOHzGyVmc0vuzxJzOxiM3vGzO4ruyxpzGxHM7vZzH4a/tt/quwyJTGzrczsR2Z2\nb1jWL5RdpkbMrM/M7jaz77Z7LAWN9pzn7nu5+z7Ad4HPl12gFD8A3uruewEPA2eUXJ409wFHAreW\nXZA4ZtYHfAWYC+wBHG9me5RbqkRfBw4tuxAZbAT+yt33APYH/qzCf9NXgAPdfW9gH+BQM9u/5DI1\n8inggTwOpKDRBnd/MfLwtUBlexW4+/fdfWP48A5ghzLLk8bdH3D3h8ouR4r9gFXu/qi7vwpcARxR\ncpliufutwHNll6MRd3/K3X8c3n+J4AJXyRW0PPBy+HBieKvsd9/MdgDeB3wtj+MpaLTJzL5oZquB\nD1HtmkbUR4Abyy5EF+sHVkcer6GiF7huZGazgDnAneWWJFnY3HMP8AzwA3evbFmBC4DPAiN5HExB\nowEzu8nM7ou5HQHg7p9z9x2By4BPVrms4T6fI2gKuKy8kmYrq4w/ZjYFuBo4ra4mXynuvilslt4B\n2M/M3lp2meKY2fuBZ9x9RV7H1BrhDbj7wRl3vQy4AfjbAouTqlFZzewk4P3AQV7yAJ0m/q5VNATs\nGHm8Q/ictMHMJhIEjMvc/Zqyy5OFu681s5sJ8kZV7GxwAHC4mR0GbAVsbWbfdPcPt3pA1TTaYGa7\nRR4eATxYVlkaMbNDCaqoh7v7urLL0+XuAnYzs53NbBJwHHBdyWXqamZmwEXAA+7+z2WXJ42ZTa/1\nPjSzycB7qeh3393PcPcd3H0Wwed0eTsBAxQ02rUwbFL5CfAHBD0UqurLwOuAH4RdhBeVXaAkZvYB\nM1sDvBO43syWll2mqLBDwSeBpQQJ22+7+/3lliqemV0O/BCYbWZrzOyjZZcpwQHAHwMHhp/Pe8Jf\nx1W0PXBz+L2/iyCn0XZX1m6haURERCQz1TRERCQzBQ0REclMQUNERDJT0BARkcwUNEREJDMFDZEW\nmNmmsFvofWZ2lZm9Jnz+TWZ2hZk9YmYrzOwGM9s98rrTzOzXZrZNyrG/Z2Zr85iRVCRvChoirVnv\n7vu4+1uBV4FTwwFqi4Fb3H0Xd9+XYDbh7SKvO56gb/+RKcc+j2DMgkjlKGiItO82YFfgPcAGdx8d\nOOnu97r7bQBmtgswBTiLIHjEcvdlwEuFllikRQoaIm0wsy0I1tVYCbwVSJsY7jiCadRvIxihvV3K\nviKVpKAh0prJ4dTYg8ATBPMmNXI8cIW7jxBMzHd0geUTKYRmuRVpzfpwauxRZnY/8EdxO5vZnsBu\nBHN/AUwCfk4wJ5hI11BNQyQ/y4EtzeyU2hNmtpeZvYuglrHA3WeFtxnADDPbqazCirRCQUMkJ+Ea\nJR8ADg673N4PfAl4miCfsbjuJYvD58cws9uAq4CDwplpDym25CLZaZZbERHJTDUNERHJTEFDREQy\nU9AQEZHMFDRERCQzBQ0REclMQUNERDJT0BARkcz+Hz4GfzHNfAQfAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "tXLug7tvlEAM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## 실루엣 분석 ( silhouette analysis )\n",
        "- 군집화의 성능을 평가하는 대표적인 방버으로 각 군집 간의 거리가 얼마나 효율적으로 분리되어 있는지 나타냄\n",
        "- 효율적 분리 : 다른 군집과의 거리는 떨어져 있고 동일 군집끼리의 데이터는 서로 가깝게 잘 뭉쳐있다는 의미 \n"
      ]
    },
    {
      "metadata": {
        "id": "EtNUZU2bk9Xv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 176
        },
        "outputId": "da1ec2b5-d5b8-4da8-c005-a21e54bc24b5"
      },
      "cell_type": "code",
      "source": [
        "# 실루엣 분석 평가 지표 값을 구하기 위한 API 추가\n",
        "from sklearn.metrics import silhouette_samples, silhouette_score\n",
        "\n",
        "\n",
        "# iris의 모든 개별 데이터에 실루엣 계수 값을 구함 \n",
        "score_samples = silhouette_samples(iris.data, irisDF['cluster'])\n",
        "print('silhouette_samples( ) return 값의 shape' , score_samples.shape)\n",
        "\n",
        "\n",
        "# irisDF에 실루엣 계수 칼럼 추가\n",
        "irisDF['silhouette_coeff'] = score_samples\n",
        "\n",
        "# 모든 데이터의 평균 실루엣 계수 값을 구함 \n",
        "average_score = silhouette_score(iris.data, irisDF['cluster'])\n",
        "print('붓꽃 데이터 세트 Silhouette Analysis Score : {0:.3f}'.format(average_score))\n",
        "irisDF.head(3)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "silhouette_samples( ) return 값의 shape (150,)\n",
            "붓꽃 데이터 세트 Silhouette Analysis Score : 0.553\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "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>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>cluster</th>\n",
              "      <th>target</th>\n",
              "      <th>pca_x</th>\n",
              "      <th>pca_y</th>\n",
              "      <th>silhouette_coeff</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.684126</td>\n",
              "      <td>0.319397</td>\n",
              "      <td>0.852955</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.714142</td>\n",
              "      <td>-0.177001</td>\n",
              "      <td>0.815495</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>-2.888991</td>\n",
              "      <td>-0.144949</td>\n",
              "      <td>0.829315</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width  cluster  target  \\\n",
              "0           5.1          3.5           1.4          0.2        1       0   \n",
              "1           4.9          3.0           1.4          0.2        1       0   \n",
              "2           4.7          3.2           1.3          0.2        1       0   \n",
              "\n",
              "      pca_x     pca_y  silhouette_coeff  \n",
              "0 -2.684126  0.319397          0.852955  \n",
              "1 -2.714142 -0.177001          0.815495  \n",
              "2 -2.888991 -0.144949          0.829315  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "metadata": {
        "id": "4OhgGeWom9tx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 102
        },
        "outputId": "536c963d-7190-4194-d276-7187de4b5c8d"
      },
      "cell_type": "code",
      "source": [
        "irisDF.groupby('cluster')['silhouette_coeff'].mean()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "cluster\n",
              "0    0.417320\n",
              "1    0.798140\n",
              "2    0.451105\n",
              "Name: silhouette_coeff, dtype: float64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    }
  ]
}