Skip to content

Instantly share code, notes, and snippets.

@jianchao-li
Created December 13, 2018 15:04
Show Gist options
  • Star 4 You must be signed in to star a gist
  • Fork 0 You must be signed in to fork a gist
  • Save jianchao-li/7065b510305d4df48063a801e6433213 to your computer and use it in GitHub Desktop.
Save jianchao-li/7065b510305d4df48063a801e6433213 to your computer and use it in GitHub Desktop.
Adaptive K-Means Clustering
Display the source blob
Display the rendered blob
Raw
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"# An implementation of the adaptive k-means clustering algorithm from the following paper.\n",
"# S. K. Bhatia. Adaptive K-Means Clustering. Proceedings of the FLAIRS 2004 Conference, \n",
"# Miami Beach, FL. May 2004."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import copy\n",
"import math\n",
"import random\n",
"import numbers\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib notebook"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"def element_distance(elem1, elem2):\n",
" \"\"\"\n",
" Euclidean distance between two elements.\n",
" \"\"\"\n",
" diff = elem1 - elem2\n",
" sum_squares = 0.\n",
" for prop in diff.get_properties():\n",
" sum_squares += prop ** 2\n",
" distance = math.sqrt(sum_squares)\n",
" return distance"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"def element_cluster_distance(element, cluster):\n",
" \"\"\"\n",
" Euclidean distance between an element and a cluster, which is just\n",
" the distance between the element and the centroid of the cluster.\n",
" \"\"\"\n",
" return element_distance(element, cluster.get_centroid())"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"def cluster_distance(cluster1, cluster2):\n",
" \"\"\"\n",
" Euclidean distance between two clusters, which is just the distance\n",
" between their centroids.\n",
" \"\"\"\n",
" return element_distance(cluster1.get_centroid(), cluster2.get_centroid())"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"def find_closest_cluster(element, clusters):\n",
" \"\"\"\n",
" Find the closest cluster to an element and the distance.\n",
" \"\"\"\n",
" closest_cluster = None\n",
" min_distance = float('inf')\n",
" for cluster in clusters:\n",
" distance = element_cluster_distance(element, cluster)\n",
" if distance == 0:\n",
" # Case 1 in the paper\n",
" # \"If the distance of the element from a\n",
" # cluster is 0, assign the element to that\n",
" # cluster, ...\"\n",
" return cluster, 0\n",
" if distance < min_distance:\n",
" min_distance = distance\n",
" closest_cluster = cluster\n",
" return closest_cluster, min_distance"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"def find_closest_clusters(clusters):\n",
" \"\"\"\n",
" Find the two closest clusters and the distance.\n",
" \"\"\"\n",
" cluster1, cluster2 = None, None # C_{m_1} and C_{m_2} in the paper\n",
" min_distance = float('inf')\n",
" n = len(clusters)\n",
" for i in range(n - 1):\n",
" for j in range(i + 1, n):\n",
" distance = cluster_distance(clusters[i], clusters[j])\n",
" if distance < min_distance:\n",
" min_distance = distance\n",
" cluster1, cluster2 = clusters[i], clusters[j]\n",
" return cluster1, cluster2, min_distance"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"def generate_clusters(num_elements, num_dims, num_clusters):\n",
" \"\"\"\n",
" Generate specified number of elements with specified number of dimensions in\n",
" specified number of clusters for the clustering algorithm.\n",
" \"\"\"\n",
" assert num_elements >= num_clusters, \\\n",
" 'Number of elements should not be smaller than number of clusters'\n",
" clusters = []\n",
" elements = []\n",
" # sample cluster boundaries to determine the number of elements in each cluster\n",
" boundaries = [0, num_elements]\n",
" for _ in range(num_clusters - 1):\n",
" while True:\n",
" boundary = np.random.randint(1, num_elements)\n",
" if not boundary in boundaries:\n",
" break\n",
" boundaries.append(boundary)\n",
" boundaries = sorted(boundaries)\n",
" # Generate elements for each cluster\n",
" for start, end in zip(boundaries[:-1], boundaries[1:]):\n",
" # Generate an initial element, each following one is by adding a random bias to it\n",
" initial = np.random.randint(low=0, high=10, size=(2,))\n",
" for _ in range(start, end):\n",
" # Make the elements lie in a circle whose radius is random\n",
" # and also proportional to the number of elements\n",
" radius = 10. * (end - start) / num_elements * random.random()\n",
" bias = (np.random.uniform(size=(2,)) * 2 - 1) * radius\n",
" bias[1] = math.sqrt(radius ** 2 - bias[0] ** 2)\n",
" if random.random() < 0.5:\n",
" bias[1] *= -1\n",
" elements.append(Element(initial + bias))\n",
" # Generate one cluster using above elements\n",
" cluster = Cluster(elements[start])\n",
" for element in elements[start + 1: end]:\n",
" cluster.add(element)\n",
" clusters.append(cluster)\n",
" random.shuffle(elements)\n",
" return clusters, elements"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"def visualize(clusters, title):\n",
" \"\"\"\n",
" Plot the clusters with a title.\n",
" \"\"\"\n",
" plt.figure(figsize=(16, 8))\n",
" for cluster in clusters:\n",
" elements = cluster.get_elements()\n",
" x, y = [], []\n",
" for element in elements:\n",
" properties = element.get_properties()\n",
" x.append(properties[0])\n",
" y.append(properties[1])\n",
" plt.scatter(x, y)\n",
" plt.title(title)\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {},
"outputs": [],
"source": [
"class Element(object):\n",
" \"\"\"\n",
" Element represents a data point with multiple properties.\n",
" \"\"\"\n",
" def __init__(self, properties):\n",
" assert isinstance(properties, np.ndarray)\n",
" self.properties = properties\n",
"\n",
" def __len__(self):\n",
" return len(self.properties)\n",
" \n",
" def __str__(self):\n",
" return '(' + ', '.join(map(str, self.properties)) + ')'\n",
" \n",
" def get_properties(self):\n",
" return self.properties\n",
" \n",
" def __add__(self, other):\n",
" assert isinstance(other, Element)\n",
" return Element(self.get_properties() + other.get_properties())\n",
" \n",
" def __sub__(self, other):\n",
" assert isinstance(other, Element)\n",
" return Element(self.get_properties() - other.get_properties())\n",
" \n",
" def __mul__(self, other):\n",
" assert isinstance(other, numbers.Number)\n",
" return Element(self.get_properties() * other)\n",
" \n",
" def __truediv__(self, other):\n",
" assert isinstance(other, numbers.Number)\n",
" return Element(self.get_properties() / other)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"class Cluster(object):\n",
" \"\"\"\n",
" Cluster represents a cluster which may include multiple elements and\n",
" a computed centroid.\n",
" \"\"\"\n",
" def __init__(self, element):\n",
" assert isinstance(element, Element)\n",
" self.elements = [element]\n",
" self.centroid = copy.deepcopy(element)\n",
" \n",
" def __len__(self):\n",
" return len(self.elements)\n",
" \n",
" def __str__(self):\n",
" return 'elements: ' + ', '.join(map(str, self.elements)) + '; centroid: ' + str(self.centroid)\n",
" \n",
" def add(self, element):\n",
" self.centroid *= len(self)\n",
" self.centroid += element\n",
" self.elements.append(element)\n",
" self.centroid /= len(self)\n",
" \n",
" def remove(self, element):\n",
" self.centroid *= len(self)\n",
" self.centroid -= element\n",
" self.elements.remove(element)\n",
" if len(self) == 0:\n",
" self.centroid = Element(np.zeros_like(self.centroid.get_properties()))\n",
" else:\n",
" self.centroid /= len(self)\n",
"\n",
" def get_elements(self):\n",
" return self.elements\n",
" \n",
" def get_centroid(self):\n",
" return self.centroid"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [],
"source": [
"class Clustering(object):\n",
" \"\"\"\n",
" Adaptive K-Means Clustering.\n",
" \"\"\"\n",
" def __init__(self, elements, num_clusters):\n",
" assert len(elements) >= num_clusters, 'Number of elements should not be smaller than number of clusters'\n",
" self.initialize_clusters(elements, num_clusters)\n",
" self.update()\n",
"\n",
" def __len__(self):\n",
" return len(self.clusters)\n",
" \n",
" def initialize_clusters(self, elements, num_clusters):\n",
" \"\"\"\n",
" Intialize the specified number of cluster seeds randomly.\n",
" \"\"\"\n",
" random.shuffle(elements)\n",
" seeds = elements[:num_clusters]\n",
" self.clusters = [Cluster(seed) for seed in seeds]\n",
" self.elements = elements[num_clusters:]\n",
" \n",
" def update(self):\n",
" self.cluster1, self.cluster2, self.min_distance = find_closest_clusters(self.clusters)\n",
" \n",
" def add(self, element):\n",
" \"\"\"\n",
" Add an unclustered element into the clustering.\n",
" \"\"\"\n",
" closest_cluster, min_distance = find_closest_cluster(element, self.clusters)\n",
" if min_distance <= self.min_distance:\n",
" # Case 2 in the paper\n",
" # \"If the distance of the element from a cluster is less than the distance\n",
" # d_{min}, assign this element to its closest cluster.\"\n",
" closest_cluster.add(element)\n",
" else:\n",
" # Case 3 in the paper\n",
" # \"merge C_{m_2} into C_{m_1}. Also, we destroy the cluster C_{m_2} by removing\n",
" # all the elements from the cluster... we add the new element into this now empty\n",
" # cluster.\"\n",
" for element2 in self.cluster2.get_elements():\n",
" self.cluster1.add(element2)\n",
" self.cluster2.remove(element2)\n",
" self.cluster2.add(element)\n",
" self.update()\n",
"\n",
" def cluster(self):\n",
" while self.elements:\n",
" self.add(self.elements.pop(0))\n",
"\n",
" def get_clusters(self):\n",
" return self.clusters"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 1152x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"if __name__ == '__main__':\n",
" num_elements = 1000\n",
" num_dims = 2\n",
" num_clusters = 10\n",
" clusters_gt, elements = generate_clusters(num_elements, num_dims, num_clusters)\n",
" clustering = Clustering(elements, num_clusters)\n",
" clustering.cluster()\n",
" clusters_akm = clustering.get_clusters()\n",
" visualize(clusters_gt, 'Ground truth clusters')\n",
" visualize(clusters_akm, 'Clusters generated by adaptive k-means')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.7.0"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment