diff --git a/clustering/affinity_propagation.ipynb b/clustering/affinity_propagation.ipynb
new file mode 100644
index 0000000..7139367
--- /dev/null
+++ b/clustering/affinity_propagation.ipynb
@@ -0,0 +1,229 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "from itertools import cycle\n",
+    "from sklearn.cluster import AffinityPropagation\n",
+    "import sklearn\n",
+    "from sklearn.preprocessing import StandardScaler\n",
+    "import imageio\n",
+    "from io import BytesIO"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def make_gif(figures, filename, fps=10, **kwargs):\n",
+    "    images = []\n",
+    "    for fig in figures:\n",
+    "        output = BytesIO()\n",
+    "        fig.savefig(output)\n",
+    "        plt.close(fig)  \n",
+    "        output.seek(0)\n",
+    "        images.append(imageio.imread(output))\n",
+    "    imageio.mimsave(filename, images, fps=fps, **kwargs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "n = 50\n",
+    "size = (n, 2)\n",
+    "\n",
+    "x = np.random.normal(0, 1, size)\n",
+    "x = np.append(x, np.random.normal(5, 1, size), axis=0)\n",
+    "c = ['r' for _ in range(n)] + ['b' for _ in range(n)]\n",
+    "plt.scatter(x[:, 0], x[:, 1], c=c)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def similarity(xi, xj):\n",
+    "    return -((xi - xj)**2).sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "def create_matrices():\n",
+    "    S = np.zeros((x.shape[0], x.shape[0]))\n",
+    "    R = np.array(S)\n",
+    "    A = np.array(S)\n",
+    "    # when looking in row i, the value means you should compare to column i - value\n",
+    "    for i in range(x.shape[0]):\n",
+    "        for j in range(x.shape[0]):\n",
+    "            S[i, j] = similarity(x[i], x[j])\n",
+    "            \n",
+    "    return A, R, S\n",
+    "\n",
+    "A, R, S = create_matrices()\n",
+    "S"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "A, R, S = create_matrices()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$ r(i, k) \\leftarrow - \\max\\limits_{k' s.t. k' \\neq k}\\{ a(i, k') + s(i, k') \\}$"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def update_r(lmda=0.9):    \n",
+    "    for i in range(x.shape[0]):\n",
+    "        for k in range(x.shape[0]):\n",
+    "            v = S[i, :] + A[i, :]\n",
+    "            v[k] = -np.inf\n",
+    "            v[i]= -np.inf\n",
+    "            R[i, k] = R[i, k] * lmda + (1 - lmda) * (S[i, k] - np.max(v))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "$ a(i, k) \\leftarrow \\min\\{0, r(k,k) + \\sum\\limits_{i' s.t. i' \\notin \\{i, k\\}}{\\max\\{0, r(i', k)\\}}$ "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "def update_a(lmda=0.9):\n",
+    "    for i in range(x.shape[0]):\n",
+    "        for k in range(x.shape[0]):\n",
+    "            v = np.array(R[:, k])\n",
+    "            if i != k:\n",
+    "                v[i] = -np.inf\n",
+    "                v[k] = - np.inf\n",
+    "                v[v < 0] = 0\n",
+    "\n",
+    "                A[i, k] = A[i, k] * lmda + (1 - lmda) * min(0, R[k, k] + v.sum())\n",
+    "            else:\n",
+    "                v[k] = -np.inf\n",
+    "                v[v < 0] = 0\n",
+    "                A[k, k] = A[k, k] * lmda + (1 - lmda) * v.sum()\n",
+    "\n",
+    "def plot_iteration(A, R):\n",
+    "    fig = plt.figure(figsize=(12, 6))\n",
+    "    sol = A + R\n",
+    "    labels = []\n",
+    "    for i in range(x.shape[0]):\n",
+    "        labels.append(np.argmax(sol[i]))\n",
+    "\n",
+    "    exemplars = np.unique(labels)\n",
+    "    colors = dict(zip(exemplars, cycle('bgrcmyk')))\n",
+    "    \n",
+    "    for i in range(len(labels)):\n",
+    "        X = x[i][0]\n",
+    "        Y = x[i][1]\n",
+    "        \n",
+    "        if i in exemplars:\n",
+    "            exemplar = i\n",
+    "            edge = 'k'\n",
+    "            ms = 10\n",
+    "        else:\n",
+    "            exemplar = labels[i]\n",
+    "            ms = 3\n",
+    "            edge = None\n",
+    "            plt.plot([X, x[exemplar][0]], [Y, x[exemplar][1]], c=colors[exemplar])\n",
+    "        plt.plot(X, Y, 'o', markersize=ms,  markeredgecolor=edge, c=colors[exemplar])\n",
+    "        \n",
+    "\n",
+    "    plt.title('Number of exemplars: %s' % len(exemplars))\n",
+    "    return fig, labels, exemplars\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "A, R, S = create_matrices()\n",
+    "preference = np.median(S)\n",
+    "\n",
+    "np.fill_diagonal(S, preference)\n",
+    "damping = 0.5\n",
+    "\n",
+    "figures = []\n",
+    "for i in range(20):\n",
+    "    update_r(damping)\n",
+    "    update_a(damping)\n",
+    "    \n",
+    "    if i % 5 == 0:\n",
+    "        fig, labels, exemplars = plot_iteration(A, R)\n",
+    "        figures.append(fig)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "make_gif(figures, 'test.gif', 2)"
+   ]
+  }
+ ],
+ "metadata": {
+  "git": {
+   "suppress_outputs": true
+  },
+  "kernelspec": {
+   "display_name": "Python [conda root]",
+   "language": "python",
+   "name": "conda-root-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.3"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
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