DBSCAN-algo.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9d791e32-c75e-4f74-ac17-67f2dd1bd72d",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from IPython.display import display_html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "35207e64-253c-4c5d-8336-07d4cbe69c93",
   "metadata": {},
   "outputs": [],
   "source": [
    "def toy_dataset():\n",
    "    value= np.array([[1, 3], [4, 2], [2, 3],[6, 7], [8, 9],[10,12],[10,15],\n",
    "    [50, 19], [20, 20], [44, 21],[46, 18], [51, 19],\n",
    "    [50, 10], [40, 22], [50, 23],[49, 53], [50, 25],\n",
    "    [50, 50], [50, 45], [44, 51],[46, 55], [51, 48],\n",
    "    [50,58], [40, 49], [50, 55],[49, 53], [50, 52],\n",
    "    [25, 80], [100, 30],[150, 90]])\n",
    "    titles = ['x','y']\n",
    "    data = pd.DataFrame(value,columns=titles)\n",
    "    print(\"Do you want to view data?\")\n",
    "    option=input()\n",
    "    if option=='yes':\n",
    "        print(\"First five data points:\")\n",
    "        display_html(data.head())\n",
    "    print(\"Do you want to view scatter plot of data?\")\n",
    "    option=input()\n",
    "    if option=='yes':\n",
    "        print(\"Data points scatter plot:\")\n",
    "        data.plot.scatter(x='x',y='y')\n",
    "    return data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "912db99a-9efd-4756-a35c-fc19640d467c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Dbscan_clustering(data):\n",
    "    db = DBSCAN(eps=10.5, min_samples=4).fit(data)\n",
    "    core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "    core_samples_mask[db.core_sample_indices_] = True\n",
    "    labels = pd.DataFrame(db.labels_,columns=['Cluster ID'])\n",
    "    result = pd.concat((data,labels), axis=1)\n",
    "    result.plot.scatter(x='x',y='y',c='Cluster ID', colormap='jet')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "9d4352db-44c7-49c9-9cf9-894025d05ec6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to view data?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " yes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First five data points:\n"
     ]
    },
    {
     "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>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Do you want to view scatter plot of data?\n"
     ]
    },
    {
     "name": "stdin",
     "output_type": "stream",
     "text": [
      " yes\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Data points scatter plot:\n",
      "Clusters contructed by DBSCAN:\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "def main():\n",
    "    data=toy_dataset()\n",
    "    print(\"Clusters contructed by DBSCAN:\")\n",
    "    Dbscan_clustering(data)\n",
    "main()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c4e9c00-5d02-4305-93a6-a224ade45b04",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
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