--- /dev/null
+{
+ "metadata": {
+ "name": "",
+ "signature": "sha256:d19cfde089d4d117d86b9df8c487e793378a7a39aae43b91a5891191362825bb"
+ },
+ "nbformat": 3,
+ "nbformat_minor": 0,
+ "worksheets": [
+ {
+ "cells": [
+ {
+ "cell_type": "heading",
+ "level": 1,
+ "metadata": {},
+ "source": [
+ "Section 22.a. Data mining II: Descriptive tasks"
+ ]
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Hierarchical clustering"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "In this notebook, you will work through the examples of hierarchical clustering."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# Standard imports\n",
+ "\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "\n",
+ "import pandas as pd\n",
+ "\n",
+ "%pylab inline\n",
+ "\n",
+ "\n",
+ "np.set_printoptions(precision=2, linewidth=100)"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "output_type": "stream",
+ "stream": "stdout",
+ "text": [
+ "Populating the interactive namespace from numpy and matplotlib\n"
+ ]
+ },
+ {
+ "output_type": "stream",
+ "stream": "stderr",
+ "text": [
+ "/Users/agw96/virtualenv_environments/tm351/lib/python3.3/site-packages/pandas/io/excel.py:626: UserWarning: Installed openpyxl is not supported at this time. Use >=1.6.1 and <2.0.0.\n",
+ " .format(openpyxl_compat.start_ver, openpyxl_compat.stop_ver))\n"
+ ]
+ }
+ ],
+ "prompt_number": 1
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "To see the hierarchical clustering techniques in action, we will use the appropriate tools from the `scipy` library."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "import scipy.spatial.distance as ssd\n",
+ "import scipy.cluster.hierarchy as sch\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [],
+ "prompt_number": 2
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "First, we will build a dataframe which contains the example data. "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "q=pd.DataFrame(data=[(4, 8), (2, 7), (1, 5), (1, 6), (4, 5), (5, 2), (8, 3), (9, 2), (6, 1), (8, 4)], \n",
+ " columns=['Attribute 1', 'Attribute 2'],\n",
+ " index=['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J'])\n",
+ "\n",
+ "q"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Attribute 1</th>\n",
+ " <th>Attribute 2</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>A</th>\n",
+ " <td> 4</td>\n",
+ " <td> 8</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>B</th>\n",
+ " <td> 2</td>\n",
+ " <td> 7</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>C</th>\n",
+ " <td> 1</td>\n",
+ " <td> 5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>D</th>\n",
+ " <td> 1</td>\n",
+ " <td> 6</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>E</th>\n",
+ " <td> 4</td>\n",
+ " <td> 5</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>F</th>\n",
+ " <td> 5</td>\n",
+ " <td> 2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>G</th>\n",
+ " <td> 8</td>\n",
+ " <td> 3</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>H</th>\n",
+ " <td> 9</td>\n",
+ " <td> 2</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>I</th>\n",
+ " <td> 6</td>\n",
+ " <td> 1</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>J</th>\n",
+ " <td> 8</td>\n",
+ " <td> 4</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 3,
+ "text": [
+ " Attribute 1 Attribute 2\n",
+ "A 4 8\n",
+ "B 2 7\n",
+ "C 1 5\n",
+ "D 1 6\n",
+ "E 4 5\n",
+ "F 5 2\n",
+ "G 8 3\n",
+ "H 9 2\n",
+ "I 6 1\n",
+ "J 8 4"
+ ]
+ }
+ ],
+ "prompt_number": 3
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "The library `scipy.spatial.distance` contains a function `pdist` which calculates the distance matrix. Use `squareform` to convert to a square format:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# compute proximity matrix\n",
+ "\n",
+ "distanceMatrix = ssd.pdist(q)\n",
+ "\n",
+ "ssd.squareform(distanceMatrix)\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 4,
+ "text": [
+ "array([[ 0. , 2.24, 4.24, 3.61, 3. , 6.08, 6.4 , 7.81, 7.28, 5.66],\n",
+ " [ 2.24, 0. , 2.24, 1.41, 2.83, 5.83, 7.21, 8.6 , 7.21, 6.71],\n",
+ " [ 4.24, 2.24, 0. , 1. , 3. , 5. , 7.28, 8.54, 6.4 , 7.07],\n",
+ " [ 3.61, 1.41, 1. , 0. , 3.16, 5.66, 7.62, 8.94, 7.07, 7.28],\n",
+ " [ 3. , 2.83, 3. , 3.16, 0. , 3.16, 4.47, 5.83, 4.47, 4.12],\n",
+ " [ 6.08, 5.83, 5. , 5.66, 3.16, 0. , 3.16, 4. , 1.41, 3.61],\n",
+ " [ 6.4 , 7.21, 7.28, 7.62, 4.47, 3.16, 0. , 1.41, 2.83, 1. ],\n",
+ " [ 7.81, 8.6 , 8.54, 8.94, 5.83, 4. , 1.41, 0. , 3.16, 2.24],\n",
+ " [ 7.28, 7.21, 6.4 , 7.07, 4.47, 1.41, 2.83, 3.16, 0. , 3.61],\n",
+ " [ 5.66, 6.71, 7.07, 7.28, 4.12, 3.61, 1. , 2.24, 3.61, 0. ]])"
+ ]
+ }
+ ],
+ "prompt_number": 4
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "And the dendrogram can then be created:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "# generate dendrogram\n",
+ "\n",
+ "sch.dendrogram(sch.linkage(distanceMatrix, method='single'), labels=q.index)\n",
+ "\n",
+ "# display dendrogram\n",
+ "plt.show()\n",
+ "\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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+ "text": [
+ "<matplotlib.figure.Figure at 0x10c396c50>"
+ ]
+ }
+ ],
+ "prompt_number": 5
+ },
+ {
+ "cell_type": "heading",
+ "level": 3,
+ "metadata": {},
+ "source": [
+ "Exercise 25.2"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "We can now try the same task with a slightly more complicated data set. We will use the football results from the 2013/2014 premier league."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "premierLeague=pd.DataFrame([(68, 41), (39, 61), (32, 74), (71, 27),\n",
+ " (33, 48), (61, 39), (40, 85), (38, 53), \n",
+ " (101, 50), (102, 37), (64, 43), (43, 59), \n",
+ " (28, 62), (54, 46), (45, 52), (41, 60), \n",
+ " (54, 54), (55, 51), (43, 59), (40, 51)],\n",
+ " index=['Arsenal', 'Aston Villa', 'Cardiff City', 'Chelsea', \n",
+ " 'Crystal Palace', 'Everton', 'Fulham', 'Hull City', \n",
+ " 'Liverpool', 'Manchester City', 'Manchester United',\n",
+ " 'Newcastle United', 'Norwich City', 'Southampton',\n",
+ " 'Stoke City', 'Sunderland', 'Swansea City',\n",
+ " 'Tottenham Hotspur', 'West Bromwich Albion',\n",
+ " 'West Ham United'],\n",
+ " columns=['Goals for', 'Goals against'])\n",
+ "\n",
+ "premierLeague\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "html": [
+ "<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
+ "<table border=\"1\" class=\"dataframe\">\n",
+ " <thead>\n",
+ " <tr style=\"text-align: right;\">\n",
+ " <th></th>\n",
+ " <th>Goals for</th>\n",
+ " <th>Goals against</th>\n",
+ " </tr>\n",
+ " </thead>\n",
+ " <tbody>\n",
+ " <tr>\n",
+ " <th>Arsenal</th>\n",
+ " <td> 68</td>\n",
+ " <td> 41</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Aston Villa</th>\n",
+ " <td> 39</td>\n",
+ " <td> 61</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Cardiff City</th>\n",
+ " <td> 32</td>\n",
+ " <td> 74</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Chelsea</th>\n",
+ " <td> 71</td>\n",
+ " <td> 27</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Crystal Palace</th>\n",
+ " <td> 33</td>\n",
+ " <td> 48</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Everton</th>\n",
+ " <td> 61</td>\n",
+ " <td> 39</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Fulham</th>\n",
+ " <td> 40</td>\n",
+ " <td> 85</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Hull City</th>\n",
+ " <td> 38</td>\n",
+ " <td> 53</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Liverpool</th>\n",
+ " <td> 101</td>\n",
+ " <td> 50</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Manchester City</th>\n",
+ " <td> 102</td>\n",
+ " <td> 37</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Manchester United</th>\n",
+ " <td> 64</td>\n",
+ " <td> 43</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Newcastle United</th>\n",
+ " <td> 43</td>\n",
+ " <td> 59</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Norwich City</th>\n",
+ " <td> 28</td>\n",
+ " <td> 62</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Southampton</th>\n",
+ " <td> 54</td>\n",
+ " <td> 46</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Stoke City</th>\n",
+ " <td> 45</td>\n",
+ " <td> 52</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Sunderland</th>\n",
+ " <td> 41</td>\n",
+ " <td> 60</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Swansea City</th>\n",
+ " <td> 54</td>\n",
+ " <td> 54</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>Tottenham Hotspur</th>\n",
+ " <td> 55</td>\n",
+ " <td> 51</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>West Bromwich Albion</th>\n",
+ " <td> 43</td>\n",
+ " <td> 59</td>\n",
+ " </tr>\n",
+ " <tr>\n",
+ " <th>West Ham United</th>\n",
+ " <td> 40</td>\n",
+ " <td> 51</td>\n",
+ " </tr>\n",
+ " </tbody>\n",
+ "</table>\n",
+ "</div>"
+ ],
+ "metadata": {},
+ "output_type": "pyout",
+ "prompt_number": 6,
+ "text": [
+ " Goals for Goals against\n",
+ "Arsenal 68 41\n",
+ "Aston Villa 39 61\n",
+ "Cardiff City 32 74\n",
+ "Chelsea 71 27\n",
+ "Crystal Palace 33 48\n",
+ "Everton 61 39\n",
+ "Fulham 40 85\n",
+ "Hull City 38 53\n",
+ "Liverpool 101 50\n",
+ "Manchester City 102 37\n",
+ "Manchester United 64 43\n",
+ "Newcastle United 43 59\n",
+ "Norwich City 28 62\n",
+ "Southampton 54 46\n",
+ "Stoke City 45 52\n",
+ "Sunderland 41 60\n",
+ "Swansea City 54 54\n",
+ "Tottenham Hotspur 55 51\n",
+ "West Bromwich Albion 43 59\n",
+ "West Ham United 40 51"
+ ]
+ }
+ ],
+ "prompt_number": 6
+ },
+ {
+ "cell_type": "heading",
+ "level": 4,
+ "metadata": {},
+ "source": [
+ "MIN criterion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# compute proximity matrix\n",
+ "distanceMatrix = ssd.pdist(premierLeague)\n",
+ "# generate dendrogram\n",
+ "sch.dendrogram(sch.linkage(distanceMatrix, method='single'), labels=premierLeague.index, orientation='right')\n",
+ "# display dendrogram\n",
+ "plt.show()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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oIGk8hRCiGoSGhqLX6zGZTJjNZr755psH3mdFo/QA1q5dy4wZMx742Pd7/LpC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\n9+/fJzMz86F6d+/e5caNG/Tp04fPP/+c33//HYC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+ "text": [
+ "<matplotlib.figure.Figure at 0x10c39b390>"
+ ]
+ }
+ ],
+ "prompt_number": 7
+ },
+ {
+ "cell_type": "heading",
+ "level": 4,
+ "metadata": {},
+ "source": [
+ "MAX criterion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# compute proximity matrix\n",
+ "distanceMatrix = ssd.pdist(premierLeague)\n",
+ "# generate dendrogram\n",
+ "sch.dendrogram(sch.linkage(distanceMatrix, method='complete'), labels=premierLeague.index, orientation='right')\n",
+ "# display dendrogram\n",
+ "plt.show()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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A0WgE8m6zuXTpEnZ2dnU+gq80UjyFEPcFW2vbOnvds7QnmeTm5vK3v/2N3bt3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\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x10c43a790>"
+ ]
+ }
+ ],
+ "prompt_number": 8
+ },
+ {
+ "cell_type": "heading",
+ "level": 4,
+ "metadata": {},
+ "source": [
+ "AVG criterion"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "# compute proximity matrix\n",
+ "distanceMatrix = ssd.pdist(premierLeague)\n",
+ "# generate dendrogram\n",
+ "sch.dendrogram(sch.linkage(distanceMatrix, method='average'), labels=premierLeague.index, orientation='right')\n",
+ "# display dendrogram\n",
+ "plt.show()"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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plFJq4cKF6q233lK3bt1STz75pDpz5oy2r4CAgOL+CSqdJAwJIUQVsbW1ZfTo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\nKoSdwOECsiYAAAAASUVORK5CYII=\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x10c3f70d0>"
+ ]
+ }
+ ],
+ "prompt_number": 9
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "For binary clustering, the results of MIN and AVG indicate that Liverpool and Manchester City are within one cluster while the rest is in the other cluster. The MAX result suggests that Arsenal, Chelsea, Everton, Liverpool, Manchester City, and Manchester United belong to one group and the rest belongs to other group. Data on its attribute space can be plotted with:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [
+ "\n",
+ "\n",
+ "plt.plot(premierLeague['Goals for'], premierLeague['Goals against'], 'o')\n",
+ "\n",
+ "plt.annotate('Liverpool', (premierLeague['Goals for']['Liverpool'], \n",
+ " premierLeague['Goals against']['Liverpool']),\n",
+ " horizontalalignment='center', verticalalignment='bottom')\n",
+ "\n",
+ "plt.annotate('Manchester City', (premierLeague['Goals for']['Manchester City'], \n",
+ " premierLeague['Goals against']['Manchester City']),\n",
+ " horizontalalignment='right', verticalalignment='bottom')\n",
+ "\n",
+ "plt.annotate('Arsenal', (premierLeague['Goals for']['Arsenal'],\n",
+ " premierLeague['Goals against']['Arsenal']),\n",
+ " horizontalalignment='left', verticalalignment='top')\n",
+ "\n",
+ "plt.annotate('Chelsea', (premierLeague['Goals for']['Chelsea'],\n",
+ " premierLeague['Goals against']['Chelsea']),\n",
+ " horizontalalignment='center', verticalalignment='bottom')\n",
+ "\n",
+ "plt.annotate('Everton', (premierLeague['Goals for']['Everton'],\n",
+ " premierLeague['Goals against']['Everton']),\n",
+ " horizontalalignment='right', verticalalignment='top')\n",
+ "\n",
+ "plt.annotate('Man. Utd', (premierLeague['Goals for']['Manchester United'],\n",
+ " premierLeague['Goals against']['Manchester United']),\n",
+ " horizontalalignment='left', verticalalignment='bottom')\n",
+ "\n",
+ "\n",
+ "# plt.annotate()\n",
+ "plt.show()\n"
+ ],
+ "language": "python",
+ "metadata": {},
+ "outputs": [
+ {
+ "metadata": {},
+ "output_type": "display_data",
+ "png": 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pTQCQn5+PmzdvAgBu376N3bt3w8PDw6Z1OTo6onv37khNTQUAJCYm\nws3NDYGBgTZ9rwBgy5Yt5tYLYNuf8z59+uDw4cMoKCiAiCAxMRGurq42f59+++03AEBmZib+/e9/\nY9KkSTb/Ob/nYXWMHj0a0dHRKCoqQnp6On766SfzTDBruv+v+FrVZImmf1JSkqhUKvHy8hKtVita\nrVZ27twpV69eFT8/P+ndu7fo9Xq5du2aJXZXLSkpKeLt7S1eXl7i4eEhb731loiITWu6n9FoNM9+\nsXVNFy5cEC8vL/Hy8hI3NzdZsWJFg6jr5MmT0r9/f/H09JSxY8dKXl6ezWu6deuWtG/fXm7cuGEe\ns3VNERER4urqKu7u7vLCCy9IUVGRzWsaOnSouLq6ipeXl+zdu1dEbPM+TZw4UTp37izNmjWTbt26\nySeffFJpHcuXLxeNRiMuLi6ya9cuq9S0YcMG+fLLL6Vbt27i4OAgarVaRo4cWeuaePIREZGCcDk7\nIiIFYagTESkIQ52ISEEY6kRECsJQJyJSEIY6EZGCMNSJiBSEoU5EpCD/H/uKphan3+wqAAAAAElF\nTkSuQmCC\n",
+ "text": [
+ "<matplotlib.figure.Figure at 0x10c4980d0>"
+ ]
+ }
+ ],
+ "prompt_number": 10
+ },
+ {
+ "cell_type": "code",
+ "collapsed": false,
+ "input": [],
+ "language": "python",
+ "metadata": {},
+ "outputs": []
+ }
+ ],
+ "metadata": {}
+ }
+ ]
+}
\ No newline at end of file
projects / tm351-notebooks.git / blobdiff