6.2 Non Hierarchical Clustering - K Medoids.ipynb

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 "cells": [
  {
   "cell_type": "markdown",
   "id": "79942709-3cc8-4c81-b10d-65485382ae20",
   "metadata": {},
   "source": [
    "# K-Medoids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "797fc289-a15a-429a-a5ba-beac87e3a387",
   "metadata": {},
   "source": [
    "- K-medoids adalah varian jenis clustering non-hieracial yang digunakan untuk mengatasi beberapa kekurangan yang ada di K-Means\n",
    "- K-means merupakan algoritma yang memanfaatkan perhitungan mean atau rata-rata, sehingga algoritma ini dipengaruhi oleh nilai outlier, yang menjadikan K-Means harus melakukan normalisasi data terlebih dahulu.\n",
    "- Pada k-medoids cara kerja lebih sederhana dibandingkan dengan K-Means, dimana centroid akan dipilih secara acak namun dengan nilai yang terdapat pada salah satu baris dataset, bukan mencari atau menentukan nilai baru."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9c4f7aba-e7bc-49e1-ab4c-aca3d9230a3e",
   "metadata": {},
   "source": [
    "## Tahapan K-Medoids\n",
    "\n",
    "1. Menentukan jumlah cluster yang diinginkan \n",
    "2. pilih secara acak medoid sebanyak k dari dataset yang tersedia\n",
    "3. hitung masing-masing jarak terhadap medoids\n",
    "4. melakukan iterasi medoids untuk mendapatkan cluster \n",
    "5. hitung total simgpangan. jika cluster tidak berubah dan julah jarak terdekat pada iterasi pertama lebih kecil dibandingkan iterasi kedua maka iterasi dihentikan\n",
    "6. jika tidak terpenuhi ulangi langkah ke 3 sampai 5"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "071a57f9-6da6-4225-a937-ef5a0669895a",
   "metadata": {
    "tags": []
   },
   "source": [
    "## 1. Import Library"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "a61120d6-158d-4012-9d51-2e4e44778267",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn_extra.cluster import KMedoids"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "38cbe75f-01d8-4a10-b41c-a70358a42ba1",
   "metadata": {},
   "source": [
    "## 2. Membaca Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "8e3672fd-6bef-49b1-8204-bd4adbaf6d5c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>No</th>\n",
       "      <th>Balita ke-</th>\n",
       "      <th>TB</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Balita 1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Balita 2</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Balita 3</td>\n",
       "      <td>71.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Balita 4</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Balita 5</td>\n",
       "      <td>92.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No Balita ke-    TB   BB\n",
       "0   1   Balita 1  52.0  5.8\n",
       "1   2   Balita 2  51.0  5.0\n",
       "2   3   Balita 3  71.5  8.5\n",
       "3   4   Balita 4  55.0  5.5\n",
       "4   5   Balita 5  92.5  6.5"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gizi = pd.read_excel('dataset/dataset_gizi.xlsx')\n",
    "gizi.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9cefa09b-7a90-41ec-9382-db3bd9b5c6a4",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 4 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   No          10 non-null     int64  \n",
      " 1   Balita ke-  10 non-null     object \n",
      " 2   TB          10 non-null     float64\n",
      " 3   BB          10 non-null     float64\n",
      "dtypes: float64(2), int64(1), object(1)\n",
      "memory usage: 452.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "gizi.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ab265ff-5116-4de5-921c-a028d0233b60",
   "metadata": {},
   "source": [
    "## 3. Memilih Kolom untuk Pemodelan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "0f66212d-ac8e-4edd-9717-732904641e9a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>TB</th>\n",
       "      <th>BB</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>52.0</td>\n",
       "      <td>5.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>51.0</td>\n",
       "      <td>5.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>71.5</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>55.0</td>\n",
       "      <td>5.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>92.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     TB   BB\n",
       "0  52.0  5.8\n",
       "1  51.0  5.0\n",
       "2  71.5  8.5\n",
       "3  55.0  5.5\n",
       "4  92.5  6.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gizi_x = gizi.iloc[:,2:4]\n",
    "gizi_x.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7b720bf8-3f8a-4986-bfef-c736803fd8cd",
   "metadata": {},
   "source": [
    "## 4. Visualisasi sebaran Data "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "79c491d3-79ef-4b6c-a695-f42ce9d331a5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
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      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(gizi.TB, gizi.BB, s = 10, c = 'c', marker = 'o', alpha = 1)\n",
    "plt.xlabel('TB')\n",
    "plt.ylabel('BB')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6e890505-1aa5-4843-bafd-ba788637fb5d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'np' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[2], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;66;03m# Mengubah dataframe menjadi array untuk memudahkan saat normalisasi data \u001b[39;00m\n\u001b[1;32m----> 2\u001b[0m x_array \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray(gizi_x)\n\u001b[0;32m      3\u001b[0m \u001b[38;5;28mprint\u001b[39m(x_array)\n",
      "\u001b[1;31mNameError\u001b[0m: name 'np' is not defined"
     ]
    }
   ],
   "source": [
    "# Mengubah dataframe menjadi array \n",
    "x_array = np.array(gizi_x)\n",
    "print(x_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ee0229ef-c191-4181-9f3a-3d1666384244",
   "metadata": {},
   "source": [
    "## 5. Pemodelan K-Medoids"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "722996f1-4e88-415e-9ab0-eb9b86de5cec",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "KMedoids = KMedoids(n_clusters = 2, random_state= 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b573cb6c-3a9b-47d1-b1b8-5e372ddf8299",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-1 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-1 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>KMedoids(n_clusters=2, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">KMedoids</label><div class=\"sk-toggleable__content\"><pre>KMedoids(n_clusters=2, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "KMedoids(n_clusters=2, random_state=0)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "KMedoids.fit(x_array)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44366d65-2a94-467e-947b-175ed5713fcc",
   "metadata": {},
   "source": [
    "### Hasil pemodelan cluster pusat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6d69b20e-af42-47fe-ab00-2694f68d579d",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[52.   5.8]\n",
      " [75.   8. ]]\n"
     ]
    }
   ],
   "source": [
    "print(KMedoids.cluster_centers_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5c38c7d4-79dc-495a-9772-5ab39d306e87",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "gizi['cluster'] = KMedoids.labels_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "2244f6d5-9f8c-4a01-92af-29c9d8a8e92a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "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>No</th>\n",
       "      <th>Balita ke-</th>\n",
       "      <th>TB</th>\n",
       "      <th>BB</th>\n",
       "      <th>cluster</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Balita 1</td>\n",
       "      <td>52.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Balita 2</td>\n",
       "      <td>51.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>Balita 3</td>\n",
       "      <td>71.5</td>\n",
       "      <td>8.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>Balita 4</td>\n",
       "      <td>55.0</td>\n",
       "      <td>5.5</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Balita 5</td>\n",
       "      <td>92.5</td>\n",
       "      <td>6.5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   No Balita ke-    TB   BB  cluster\n",
       "0   1   Balita 1  52.0  5.8        0\n",
       "1   2   Balita 2  51.0  5.0        0\n",
       "2   3   Balita 3  71.5  8.5        1\n",
       "3   4   Balita 4  55.0  5.5        0\n",
       "4   5   Balita 5  92.5  6.5        1"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gizi.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "08b1ab78-ac7d-4058-828e-e53105d257d5",
   "metadata": {},
   "source": [
    "## 6. Visualisasi Hasil Clustering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "815b729d-396b-40b6-a417-1ab6e931abba",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "output = plt.scatter(x_array[:,0], x_array[:,1], s= 400, c = gizi.cluster, marker = 'o', alpha = 1)\n",
    "centers = KMedoids.cluster_centers_\n",
    "plt.scatter(centers[:,0], centers[:,1], c = 'red', s =100, alpha = 1, marker = 's')\n",
    "\n",
    "plt.title('hasil clsutering k-medoids')\n",
    "plt.colorbar(output)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "009b589f-dd24-444f-907d-c57969fc2db2",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Kelebihan dan Kekurangan K-Medoids\n",
    "\n",
    "### Kelebihan \n",
    "\n",
    "1. Tidak dipengaruhi oleh outlier\n",
    "2. Mudah diimplementasikan dan dimengerti\n",
    "3. Mudah ditafsirkan\n",
    "\n",
    "#### Kekurangan\n",
    "\n",
    "1. pengaturan cluster secara manual akan menyebabkan hasil yang tidak optimal"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "vulcan_da",
   "language": "python",
   "name": "vulcan_da"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}