dbscan.ipynb

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  {
   "cell_type": "markdown",
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   "source": [
    "<h1 align='center'>DBSCAN clustering algorithm</h1>\n",
    "\n",
    "\n",
    "Finds core samples of high density and expands clusters from them.\n",
    "\n",
    "**Density-based spatial clustering of applications with noise (DBSCAN)** is a data clustering algorithm proposed by Martin Ester, Hans-Peter Kriegel, Jörg Sander and Xiaowei Xu in 1996 \n",
    "\n",
    "It is a density-based clustering non-parametric algorithm: given a set of points in some space, it groups together points that are closely packed together (points with many nearby neighbors), marking as outliers points that lie alone in low-density regions (whose nearest neighbors are too far away). DBSCAN is one of the most common clustering algorithms and also most cited in scientific literature\n",
    "\n",
    "![](https://lh3.googleusercontent.com/--gW1dobRJfk/XXoZaUmlyyI/AAAAAAAAhnM/RA2mCenM47cmMtAveDhfkw4PFOyk9MWhwCK8BGAsYHg/s0/2019-09-12.png)\n",
    "Defined distance (DBSCAN)—Uses a specified distance to separate dense clusters from sparser noise. The DBSCAN algorithm is the fastest of the clustering methods, but is only appropriate if there is a very clear Search Distance to use, and that works well for all potential clusters. This requires that all meaningful clusters have similar densities.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(algorithm='auto', eps=1, leaf_size=30, metric='euclidean',\n",
       "       metric_params=None, min_samples=2, n_jobs=None, p=None)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "import numpy as np\n",
    "X = np.array([[1, 2], [2, 2], [2, 3],[8, 7], [8, 8], [25, 80]])\n",
    "clustering = DBSCAN(eps=1, min_samples=2).fit(X)\n",
    "clustering"
   ]
  },
  {
   "cell_type": "code",
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      "text/plain": [
       "array([[1, 2],\n",
       "       [2, 2],\n",
       "       [2, 3],\n",
       "       [8, 7],\n",
       "       [8, 8]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering.components_"
   ]
  },
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       "1"
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     "execution_count": 6,
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   ],
   "source": [
    "clustering.eps"
   ]
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     "execution_count": 8,
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   ],
   "source": [
    "clustering.labels_"
   ]
  },
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       "30"
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     "execution_count": 21,
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   ],
   "source": [
    "clustering.leaf_size"
   ]
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   "cell_type": "code",
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   ],
   "source": [
    "clustering.core_sample_indices_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method BaseEstimator.get_params of DBSCAN(algorithm='auto', eps=1, leaf_size=30, metric='euclidean',\n",
       "       metric_params=None, min_samples=2, n_jobs=None, p=None)>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering.get_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'euclidean'"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clustering.metric"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "jupyter": {
     "outputs_hidden": false
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750, 2)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.cluster import DBSCAN\n",
    "from sklearn import metrics\n",
    "from sklearn.datasets.samples_generator import make_blobs\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "\n",
    "# Generate sample data\n",
    "centers = [[1, 1], [-1, -1], [1, -1]]\n",
    "X, labels_true = make_blobs(n_samples=750, centers=centers, cluster_std=0.4,\n",
    "                            random_state=0)\n",
    "\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.49426097,  1.45106697],\n",
       "       [-1.42808099, -0.83706377],\n",
       "       [ 0.33855918,  1.03875871],\n",
       "       ...,\n",
       "       [-0.05713876, -0.90926105],\n",
       "       [-1.16939407,  0.03959692],\n",
       "       [ 0.26322951, -0.92649949]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = StandardScaler().fit_transform(X)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(750, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DBSCAN(algorithm='auto', eps=0.3, leaf_size=30, metric='euclidean',\n",
       "       metric_params=None, min_samples=10, n_jobs=None, p=None)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Compute DBSCAN\n",
    "db = DBSCAN(eps=0.3, min_samples=10).fit(X)\n",
    "db"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
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     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_samples_mask = np.zeros_like(db.labels_, dtype=bool)\n",
    "core_samples_mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
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       "        2,  0,  2,  2,  2,  2, -1,  0, -1,  1,  1,  1,  1,  0,  0,  1,  0,\n",
       "        1,  2,  1,  0,  0,  1,  2,  1,  0,  0,  2,  0,  2,  2,  2,  0, -1,\n",
       "        2,  2,  0,  1,  0,  2,  0,  0,  2,  2, -1,  2,  1, -1,  2,  1,  1,\n",
       "        2,  2,  2,  0,  1,  0,  1,  0,  1,  0,  2,  2, -1,  1,  2,  2,  1,\n",
       "        0,  1,  2,  2,  2,  1,  1,  2,  2,  0,  1,  2,  0,  0,  2,  0,  0,\n",
       "        1,  0,  1,  0,  1,  1,  2,  2,  0,  0,  1,  1,  2,  1,  2,  2,  2,\n",
       "        2,  0,  2,  0,  2,  2,  0,  2,  2,  2,  0,  0,  1,  1,  1,  2,  2,\n",
       "        2,  2,  1,  2,  2,  0,  0,  2,  0,  0,  0,  1,  0,  1,  1,  1,  2,\n",
       "        1,  1,  0,  1,  2,  2,  1,  2,  2,  1,  0,  0,  1,  1,  1,  0,  1,\n",
       "        0,  2,  0,  2,  2,  2,  2,  2,  1,  1,  0,  0,  1,  1,  0,  0,  2,\n",
       "        1, -1,  2,  1,  1,  2,  1,  2,  0,  2,  2,  0,  1,  2,  2,  0,  2,\n",
       "        2,  0,  0,  2,  0,  2,  0,  2,  1,  0,  0,  0,  1,  2,  1,  2,  2,\n",
       "        0,  2,  2,  0,  0,  2,  1,  1,  1,  1,  1,  0,  1,  1,  1,  1,  0,\n",
       "        0,  1,  1,  1,  0,  2,  0,  1,  2,  2,  0,  0,  2,  0,  2,  1,  0,\n",
       "        2,  0,  2,  0,  2,  2,  0,  1,  0,  1,  0,  2,  2,  1,  1,  1,  2,\n",
       "        0,  2,  0,  2,  1,  2,  2,  0,  1,  0,  1,  0,  0,  0,  0,  2,  0,\n",
       "        2,  0,  1,  0,  1,  2,  1,  1,  1,  0,  1,  1,  0,  2,  1,  0,  2,\n",
       "        2,  1,  1,  2,  2,  2,  1,  2,  1,  2,  0,  2,  1,  2,  1,  0,  1,\n",
       "        0,  1,  1,  0,  1,  2, -1,  1,  0,  0,  2,  1,  2,  2,  2,  2,  1,\n",
       "        0,  0,  0,  0,  1,  0,  2,  1,  0,  1,  2,  0,  0,  1,  0,  1,  1,\n",
       "        0, -1,  0,  2,  2,  2,  1,  1,  2,  0,  1,  0,  0,  1,  0,  1,  1,\n",
       "        2,  2, -1,  0,  1,  2,  2,  1,  1,  1,  1,  0,  0,  0,  2,  2,  1,\n",
       "        2,  1,  0,  0,  1,  2,  1,  0,  0,  2,  0,  1,  0,  2,  1,  0,  2,\n",
       "        2,  1,  0,  0,  0,  2,  1,  1,  0,  2,  0,  0,  1,  1,  1,  1,  0,\n",
       "        1,  0,  1,  0,  0,  2,  0,  1,  1,  2,  1,  1,  0,  1,  0,  2,  1,\n",
       "        0,  0,  1,  0,  1,  1,  2,  2,  1,  2,  2,  1,  2,  1,  1,  1,  1,\n",
       "        2,  0,  0,  0,  1,  2,  2,  0,  2,  0,  2,  1,  0,  1,  1,  0,  0,\n",
       "        1,  2,  1,  2,  2,  0,  2,  1,  1,  1,  2,  0,  0,  2,  0,  2,  2,\n",
       "        0,  2,  0,  1,  1,  1,  1,  0,  0,  0,  2,  1,  1,  1,  1,  2,  2,\n",
       "        2,  0,  2,  1,  1,  0,  0,  1,  0,  2,  1,  2,  1,  0,  2,  2,  0,\n",
       "        0,  1,  0,  0,  2,  0,  0,  0,  2,  0,  2,  0,  0,  1,  1,  0,  0,\n",
       "        1,  2,  2,  0,  0,  0,  0,  2, -1,  1,  1,  2,  1,  0,  0,  2,  2,\n",
       "        0,  1,  2,  0,  1,  2,  2,  1,  0,  0, -1, -1,  2,  0,  0,  0,  2,\n",
       "       -1,  2,  0,  1,  1,  1,  1,  1,  0,  0,  2,  1,  2,  0,  1,  1,  1,\n",
       "        0,  2,  1,  1, -1,  2,  1,  2,  0,  2,  2,  1,  0,  0,  0,  1,  1,\n",
       "        2,  0,  0,  2,  2,  1,  2,  2,  2,  0,  2,  1,  2,  1,  1,  1,  2,\n",
       "        0,  2,  0,  2,  2,  0,  0,  2,  1,  2,  0,  2,  0,  0,  0,  1,  0,\n",
       "        2,  1,  2,  0,  1,  0,  0,  2,  0,  2,  1,  1,  2,  1,  0,  1,  2,\n",
       "        1,  2], dtype=int64)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "core_samples_mask[db.core_sample_indices_] = True\n",
    "labels = db.labels_\n",
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of clusters in labels, ignoring noise if present.\n",
    "n_clusters_ = len(set(labels)) - (1 if -1 in labels else 0)\n",
    "n_clusters_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n_noise_ = list(labels).count(-1)\n",
    "n_noise_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Estimated number of clusters: 3\n",
      "Estimated number of noise points: 18\n",
      "Homogeneity: 0.953\n",
      "Completeness: 0.883\n",
      "V-measure: 0.917\n",
      "Adjusted Rand Index: 0.952\n",
      "Adjusted Mutual Information: 0.916\n",
      "Silhouette Coefficient: 0.626\n"
     ]
    }
   ],
   "source": [
    "print('Estimated number of clusters: %d' % n_clusters_)\n",
    "print('Estimated number of noise points: %d' % n_noise_)\n",
    "print(\"Homogeneity: %0.3f\" % metrics.homogeneity_score(labels_true, labels))\n",
    "print(\"Completeness: %0.3f\" % metrics.completeness_score(labels_true, labels))\n",
    "print(\"V-measure: %0.3f\" % metrics.v_measure_score(labels_true, labels))\n",
    "print(\"Adjusted Rand Index: %0.3f\"\n",
    "      % metrics.adjusted_rand_score(labels_true, labels))\n",
    "print(\"Adjusted Mutual Information: %0.3f\"\n",
    "      % metrics.adjusted_mutual_info_score(labels_true, labels,\n",
    "                                           average_method='arithmetic'))\n",
    "print(\"Silhouette Coefficient: %0.3f\"\n",
    "      % metrics.silhouette_score(X, labels))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# #############################################################################\n",
    "# Plot result\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Black removed and is used for noise instead.\n",
    "unique_labels = set(labels)\n",
    "colors = [plt.cm.Spectral(each)\n",
    "          for each in np.linspace(0, 1, len(unique_labels))]\n",
    "for k, col in zip(unique_labels, colors):\n",
    "    if k == -1:\n",
    "        # Black used for noise.\n",
    "        col = [0, 0, 0, 1]\n",
    "\n",
    "    class_member_mask = (labels == k)\n",
    "\n",
    "    xy = X[class_member_mask & core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
    "             markeredgecolor='k', markersize=14)\n",
    "\n",
    "    xy = X[class_member_mask & ~core_samples_mask]\n",
    "    plt.plot(xy[:, 0], xy[:, 1], 'o', markerfacecolor=tuple(col),\n",
    "             markeredgecolor='k', markersize=6)\n",
    "\n",
    "plt.title('Estimated number of clusters: %d' % n_clusters_)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Advantages\n",
    "1. DBSCAN does not require one to specify the number of clusters in the data a priori, as opposed to k-means.\n",
    "2. DBSCAN can find arbitrarily shaped clusters. It can even find a cluster completely surrounded by (but not connected to) a different cluster. Due to the MinPts parameter, the so-called single-link effect (different clusters being connected by a thin line of points) is reduced.\n",
    "3. DBSCAN has a notion of noise, and is robust to outliers.\n",
    "4. DBSCAN requires just two parameters and is mostly insensitive to the ordering of the points in the database. (However, points sitting on the edge of two different clusters might swap cluster membership if the ordering of the points is changed, and the cluster assignment is unique only up to isomorphism.)\n",
    "5. DBSCAN is designed for use with databases that can accelerate region queries, e.g. using an R* tree.\n",
    "6 .The parameters minPts and ε can be set by a domain expert, if the data is well understood.\n",
    "\n",
    "\n",
    "#### Disadvantages\n",
    "\n",
    "1. DBSCAN is not entirely deterministic: border points that are reachable from more than one cluster can be part of either cluster, depending on the order the data are processed. For most data sets and domains, this situation does not arise often and has little impact on the clustering result:[5] both on core points and noise points, DBSCAN is deterministic. DBSCAN*[7] is a variation that treats border points as noise, and this way achieves a fully deterministic result as well as a more consistent statistical interpretation of density-connected components.\n",
    "2. The quality of DBSCAN depends on the distance measure used in the function regionQuery(P,ε). The most common distance metric used is Euclidean distance. Especially for high-dimensional data, this metric can be rendered almost useless due to the so-called \"Curse of dimensionality\", making it difficult to find an appropriate value for ε. This effect, however, is also present in any other algorithm based on Euclidean distance.\n",
    "3. DBSCAN cannot cluster data sets well with large differences in densities, since the minPts-ε combination cannot then be chosen appropriately for all clusters.[8]\n",
    "If the data and scale are not well understood, choosing a meaningful distance threshold ε can be difficult."
   ]
  }
 ],
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