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+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "collapsed": true
+ },
+ "source": [
+ "## Exercícios\n",
+ "\n",
+ "1 - Aplique os algoritmos K-means [1] e AgglomerativeClustering [2] em qualquer dataset que você desejar (recomendação: iris). Compare os resultados utilizando métricas de avaliação de clusteres (completeness e homogeneity, por exemplo) [3].\n",
+ "\n",
+ "* [1] http://scikit-learn.org/stable/modules/clustering.html#k-means\n",
+ "\n",
+ "* [2] http://scikit-learn.org/0.17/modules/clustering.html#hierarchical-clustering\n",
+ "\n",
+ "* [3] http://scikit-learn.org/stable/modules/clustering.html#clustering-evaluation\n",
+ "\n",
+ "2 - Qual o valor de K (número de clusteres) você escolheu para a questão anterior? Desenvolva o Método do Cotovelo (não utilizar lib!) e descubra o K mais adequado. Após descobrir, aplique novamente o K-means com o K adequado. \n",
+ "\n",
+ "* Ajuda: atributos do [k-means](http://scikit-learn.org/0.17/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans)\n",
+ "\n",
+ "3 - Após a questão 2, você aplicou o algoritmo com K apropriado. Refaça o cálculo das métricas de acordo com os resultados de clusters obtidos com a questão anterior e verifique se o resultado melhorou.\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import math\n",
+ "import matplotlib.pyplot as plt\n",
+ "from sklearn.decomposition import PCA\n",
+ "from sklearn.cluster import KMeans\n",
+ "from sklearn.cluster import AgglomerativeClustering\n",
+ "from sklearn import metrics\n",
+ "from sklearn import decomposition"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "headers = [\"n1\", \"n2\", \"n3\", \"n4\",\"n5\", \"level\"]\n",
+ "data = pd.read_csv(\"user_knowledge_level.csv\", header=None, names=headers)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
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