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@bbarrilleaux
Created March 28, 2014 19:36
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IPython notebook using scikit-learn for K-means clustering.
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{
"metadata": {
"name": "Iris Clustering"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": "Let's use scikit-learn for K-means clustering on [Fisher's Iris dataset](http://en.wikipedia.org/wiki/Iris_flower_data_set), and plot the resulting clusters in 3D. "
},
{
"cell_type": "code",
"collapsed": false,
"input": "%matplotlib inline\n\nfrom sklearn import datasets, cluster\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\n# note: I deliberately chose a random seed that ends up \n# labeling the clusters with the same numbering convention \n# as the original y values \nnp.random.seed(2)\n\n# load data\niris = datasets.load_iris()\n\nX_iris = iris.data\ny_iris = iris.target\n\n# do the clustering\nk_means = cluster.KMeans(n_clusters=3)\nk_means.fit(X_iris) \nlabels = k_means.labels_\n\n# check how many of the samples were correctly labeled\ncorrect_labels = sum(y_iris == labels)\n\nprint(\"Result: %d out of %d samples were correctly labeled.\" % (correct_labels, y_iris.size))\n\n# plot the clusters in color\nfig = plt.figure(1, figsize=(8, 8))\nplt.clf()\nax = Axes3D(fig, rect=[0, 0, 1, 1], elev=8, azim=200)\nplt.cla()\n\nax.scatter(X_iris[:, 3], X_iris[:, 0], X_iris[:, 2], c=labels.astype(np.float))\n\nax.w_xaxis.set_ticklabels([])\nax.w_yaxis.set_ticklabels([])\nax.w_zaxis.set_ticklabels([])\nax.set_xlabel('Petal width')\nax.set_ylabel('Sepal length')\nax.set_zlabel('Petal length')\n\nplt.show()",
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": "Result: 134 out of 150 samples were correctly labeled.\n"
},
{
"metadata": {},
"output_type": "display_data",
"png": 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frY/feivlwQkA6uvrUX9+u4CvO0gTUWZjDU9FaoWAdKpqDEcOSm63G06nEy6X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QAAAg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\niEiEffjhh3To0IHJkyebH/N4P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"text": "<matplotlib.figure.Figure at 0x1077ddd10>"
}
],
"prompt_number": 32
},
{
"cell_type": "code",
"collapsed": false,
"input": "",
"language": "python",
"metadata": {},
"outputs": []
}
],
"metadata": {}
}
]
}
@Debanjan1234
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Does k-means depends on the no of features? As in Iris dataset we have 4 features. Can I use this code for a dataset where I have 50 features ?

@D4T4R00T
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Yes, of course. You just have to refactor the feaatures, but it should work. Even though, I believe k-means is not the best approach to such quantity of features.

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