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"# Mean Shift Clustering\n",
"\n",
"Mean shift clustering aims to discover “blobs” in a smooth density of samples. It is a centroid-based algorithm, which works by updating candidates for centroids to be the mean of the points within a given region. These candidates are then filtered in a post-processing stage to eliminate near-duplicates to form the final set of centroids.\n",
"\n",
"### Working\n",
"\n",
"* Define a window (bandwidth of the kernel) and place the window on a data point\n",
"* Calculate the mean for all the points in the window\n",
"* Move the center of the window to the location of the mean\n",
"* Repeat steps 2 and 3 until there is convergence"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
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"source": [
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt"
]
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"cell_type": "code",
"execution_count": 3,
"metadata": {},
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"\n",
"\n",
"
\n",
" \n",
" \n",
" CustomerID \n",
" Genre \n",
" Age \n",
" Annual Income (k$) \n",
" Spending Score (1-100) \n",
" \n",
" \n",
" \n",
" \n",
" 127 \n",
" 128 \n",
" Male \n",
" 40 \n",
" 71 \n",
" 95 \n",
" \n",
" \n",
" 9 \n",
" 10 \n",
" Female \n",
" 30 \n",
" 19 \n",
" 72 \n",
" \n",
" \n",
"
\n",
"
"
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"fig = plt.figure(figsize=(14, 20))\n",
"\n",
"#plt.subplot(121) (for 2d)\n",
"plt.subplot(211, projection='3d')\n",
"plt.scatter(X[:, 0], X[:, 1],X[:,2], c=y_ms, cmap='prism')\n",
"centers = ms.cluster_centers_\n",
"plt.scatter(centers[:, 0], centers[:, 1],centers[:,2], c='black')\n",
"plt.title('Clusters using MeanShift')\n",
"plt.xlabel('Age')\n",
"plt.ylabel('Annual Income (k$)')\n",
"\n",
"#plt.subplot(122) (for 2d)\n",
"plt.subplot(212, projection='3d')\n",
"plt.scatter(X[:, 0], X[:, 1],X[:,2], c=y_kmeans, cmap='prism')\n",
"centers = kmean.cluster_centers_\n",
"plt.scatter(centers[:, 0], centers[:, 1], centers[:,2], c='black')\n",
"plt.xlabel('Age')\n",
"plt.title('Clusters using Kmeans')\n",
"plt.ylabel('Annual Income (k$)')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Disadvantage\n",
"computationally expensive \n",
" \n",
"O(T*n*log(n)) in lower dimensions, with n the number of samples and T the number of points \n",
"O(T*n^2) in higher dimensions"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
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