T6 - 5 - Clustering -Método del codo y el factor de la silueta..ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "from sklearn.cluster import KMeans\n",
    "from sklearn import metrics\n",
    "\n",
    "from scipy.spatial.distance import cdist\n",
    "from sklearn.metrics import silhouette_score,silhouette_samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x7f446f2e6cc0>"
      ]
     },
     "execution_count": 10,
     "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": [
    "x1=np.array([3,1,1,2,1,6,6,6,5,6,7,8,9,8,9,9,8])\n",
    "x2=np.array([5,4,5,6,5,8,6,7,6,7,1,2,1,2,3,2,3])\n",
    "X=np.array(list(zip(x1,x2))).reshape(len(x1),2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Dataset a clasificar')"
      ]
     },
     "execution_count": 12,
     "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.figure()\n",
    "plt.scatter(x1,x2)\n",
    "plt.xlim(0,10)\n",
    "plt.ylim(0,10)\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title(\"Dataset a clasificar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representación del método del codo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Representación del codo')"
      ]
     },
     "execution_count": 86,
     "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.figure()\n",
    "plt.plot(K,ssw,'ok-')\n",
    "plt.ylabel(\"SSW\")\n",
    "plt.xlabel(\"k\")\n",
    "plt.title(\"Representación del codo\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Representación del método del codo normalizado"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Representación del codo normalizado')"
      ]
     },
     "execution_count": 88,
     "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.figure()\n",
    "plt.plot(K,1-(ssw/sst),'gx-')\n",
    "plt.ylabel(\"SSW\")\n",
    "plt.xlabel(\"k\")\n",
    "plt.title(\"Representación del codo normalizado\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.8"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}