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Comparing Python and R methods of plotting KMeans clustering within sum of squares against cluster size
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{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%load_ext rpy2.ipython"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Python"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline\n",
"from sklearn.cluster import KMeans\n",
"from sklearn.datasets import load_iris\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.text.Text at 0x29140f0>"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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uRADw+98ndQY33AAf+1hZQzEzy6SzzUcvoaWVUCNJ09Hfpp+PRkRZXruthEQA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\nAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0xd872278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data = load_iris()['data']\n",
"results = []\n",
"for n_clusters in range(1, 16):\n",
" km = KMeans(n_clusters=n_clusters)\n",
" km.fit(data)\n",
" results.append({'n_clusters': n_clusters,\n",
" 'wss': km.inertia_})\n",
"pd.DataFrame(results).set_index('n_clusters').plot(marker='o')\n",
"plt.ylabel('Within groups sum of squares')\n",
"plt.xlabel('Number of Clusters')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## R"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"image/png": 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JYJ0p5YEMORHc1fwX/TWBGHIimH3ccFp/VSCavAhmv71Ff1UgmtwIZlc/q78uEEl+BB+7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\nRK5CYII=\n"
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"%%R\n",
"data(iris)\n",
"mydata <- iris[1:4]\n",
"wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))\n",
"for (i in 2:15) wss[i] <- sum(kmeans(mydata, centers=i)$withinss)\n",
"plot(1:15, wss, type=\"b\", xlab=\"Number of Clusters\", ylab=\"Within groups sum of squares\")"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 2",
"language": "python",
"name": "python2"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 2
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython2",
"version": "2.7.8"
}
},
"nbformat": 4,
"nbformat_minor": 0
}
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