Hierarchical Clustering.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Agglomerative Hierarchical Clustering \n",
    "It merges the most similar or nearest pairs of clusters upto when all data point are merged into one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(r\"C:\\Users\\ASUS\\Desktop\\Iris.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>Iris-setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Id  SepalLengthCm  SepalWidthCm  PetalLengthCm  PetalWidthCm      Species\n",
       "0   1            5.1           3.5            1.4           0.2  Iris-setosa\n",
       "1   2            4.9           3.0            1.4           0.2  Iris-setosa\n",
       "2   3            4.7           3.2            1.3           0.2  Iris-setosa\n",
       "3   4            4.6           3.1            1.5           0.2  Iris-setosa\n",
       "4   5            5.0           3.6            1.4           0.2  Iris-setosa"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X=df[\"PetalLengthCm\"]\n",
    "Y=df[\"SepalWidthCm\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "Z = np.array(list(zip(X, Y)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dendrogram Representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1440x1008 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.cluster.hierarchy as sch\n",
    "plt.figure(figsize=(20, 14))  \n",
    "plt.title(\"Dendrograms\")  \n",
    "plt.axhline(y=15,linestyle=\"--\")\n",
    "plt.show(sch.dendrogram(sch.linkage(Z, method='ward')))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Number of Clusters Required to classify the problem can be calculated by finding the longest vertical line \n",
    "from which no horizontal line passes in dendrogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AgglomerativeClustering()"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import AgglomerativeClustering\n",
    "cluster = AgglomerativeClustering(n_clusters=2)  \n",
    "cluster.fit(Z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x243708c6c88>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df['PetalLengthCm'], df['SepalWidthCm'], c=cluster.labels_) "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
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