{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Clustering Analysis\n", "In this chapter we will concern ourselves with the study of **cluster analysis**.\n", "In general terms cluster analysis, or clustering, is the task of grouping a\n", "data-set into different distinct categories based on some measure of equality of\n", "the data. This measure is often referred to as a **metric** or **similarity\n", "measure** in the literature (note: sometimes we deal with a **dissimilarity\n", "measure** instead). Usually, these metrics are formulated as some kind of\n", "distance function between points in a high-dimensional space.\n", "\n", "There exists a lot of such distance measures. The simplest, and also the most\n", "common is the **Euclidean distance** (i.e. Pythagoras). A good source for those of\n", "you wanting a thorough overview is the article (DOI:10.5120/ijca2016907841\n", "Irani, Pise, Phatak). A few other metrics mentioned there are: *cosine\n", "similarity*, *Manhattan distance*, *Chebychev distance* and the *Minkowski\n", "distance*. The Minkowski distance is a general formulation which encapsulates a\n", "range of metrics. All of these, and many more, can be used in clustering. There\n", "exists different categories of clustering algorithms. A few of the most\n", "common are: *centroid-*, *distribution-*, *density-* and *hierarchical-\n", "clustering*. We will concern ourselves primarily with the first one." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Basic Idea of the K-means Clustering Algorithm\n", "The simplest of all clustering algorithms is the aptly named **k-means algorithm**\n", ", sometimes also referred to as *Lloyds algorithm*. It is the simplest and also\n", "the most common. From its simplicity it obtains both strengths and weaknesses.\n", "These will be discussed in more detail later. The k-means algorithm is a\n", "**centroid based** clustering algorithm.\n", "\n", "Assume, we are given $n$ data points and we wish to split the data into $K < n$\n", "different categories, or clusters. We label each cluster by an integer $k\\in\\{\n", "1, \\cdots, K \\}$. In the basic k-means algorithm each point is assigned to only\n", "one cluster $k$, and these assignments are *non-injective* i.e. many-to-one. We\n", "can think of these mappings as an encoder $k = C(i)$, which assigns the $i$-th\n", "data-point $\\bf x_i$ to the $k$-th cluster. Before we jump into the mathematics\n", "let us describe the k-means algorithm in words:\n", "1. We start with guesses / random initializations of our $k$ cluster centers / centroids\n", "\n", "2. For each centroid the points that are most similar are identified\n", "\n", "3. Then we move / replace each centroid with a coordinate average of all the points that were assigned to that centroid.\n", "\n", "4. Iterate this points 2, 3) until the centroids no longer move (to some tolerance)\n", "\n", "Now we consider the method formally. Again, we assume we have $n$ data-points\n", "(vectors)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "
\n", "\n", "$$\n", "\\begin{equation}\\label{eq:kmeanspoints} \\tag{1}\n", " \\boldsymbol{x_i} = \\{x_{i, 1}, \\cdots, x_{i, p}\\}\\in\\mathbb{R}^p.\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which we wish to group into $K < n$ clusters. For our dissimilarity measure we\n", "will use the *squared Euclidean distance*" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "$$\n", "\\begin{equation}\\label{eq:squaredeuclidean} \\tag{2}\n", " d(\\boldsymbol{x_i}, \\boldsymbol{x_i'}) = \\sum_{j=1}^p(x_{ij} - x_{i'j})^2\n", " = ||\\boldsymbol{x_i} - \\boldsymbol{x_{i'}}||^2\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we define the so called *within-cluster point scatter* which gives us a\n", "measure of how close each data point assigned to the same cluster tends to be to\n", "the all the others." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "$$\n", "\\begin{equation}\\label{eq:withincluster} \\tag{3}\n", " W(C) = \\frac{1}{2}\\sum_{k=1}^K\\sum_{C(i)=k}\n", " \\sum_{C(i')=k}d(\\boldsymbol{x_i}, \\boldsymbol{x_{i'}}) =\n", " \\sum_{k=1}^KN_k\\sum_{C(i)=k}||\\boldsymbol{x_i} - \\boldsymbol{\\overline{x_k}}||^2\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where $\\boldsymbol{\\overline{x_k}}$ is the mean vector associated with the $k$-th\n", "cluster, and $N_k = \\sum_{i=1}^nI(C(i) = k)$, where the $I()$ notation is\n", "similar to the Kronecker delta (*Commonly used in statistics, it just means that\n", "when $i = k$ we have the encoder $C(i)$*). In other words, the within-cluster\n", "scatter measures the compactness of each cluster with respect to the data points\n", "assigned to each cluster. This is the quantity that the $k$-means algorithm aims\n", "to minimize. We refer to this quantity $W(C)$ as the within cluster scatter\n", "because of its relation to the *total scatter*." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "$$\n", "\\begin{equation}\\label{eq:totalscatter} \\tag{4}\n", " T = W(C) + B(C) = \\frac{1}{2}\\sum_{i=1}^n\n", " \\sum_{i'=1}^nd(\\boldsymbol{x_i}, \\boldsymbol{x_{i'}})\n", " = \\frac{1}{2}\\sum_{k=1}^K\\sum_{C(i)=k}\n", " \\Big(\\sum_{C(i') = k}d(\\boldsymbol{x_i}, \\boldsymbol{x_{i'}})\n", " + \\sum_{C(i')\\neq k}d(\\boldsymbol{x_i}, \\boldsymbol{x_{i'}})\\Big)\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which is a quantity that is conserved throughout the $k$-means algorithm. It can\n", "be thought of as the total amount of information in the data, and it is composed\n", "of the aforementioned within-cluster scatter and the *between-cluster scatter*\n", "$B(C)$. In methods such as principle component analysis the total scatter is not\n", "conserved.\n", "\n", "Given a cluster mean $\\boldsymbol{m_k}$ we define the **total cluster variance**" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "\n", "\n", "$$\n", "\\begin{equation}\\label{eq:totalclustervariance} \\tag{5}\n", " \\min_{C, \\{\\boldsymbol{m_k}\\}_1^K}\\sum_{k=1}^KN_k\\sum||\\boldsymbol{x_i} - \\boldsymbol{m_k}||^2\n", "\\end{equation}\n", "$$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we have all the pieces necessary to formally revisit the k-means algorithm.\n", "If you at this point feel like some of the above definitions came a bit out of\n", "no-where, don't fret, the method does get a whole lot simpler once we start\n", "programming." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The K-means Clustering Algorithm\n", "The k-means clustering algorithm goes as follows (note in my opinion this\n", "description is a bit complicated and is lifted directly out of ESL HASTIE for\n", "deeper understanding purposes)\n", "\n", "1. For a given cluster assignment $C$, and $k$ cluster means $\\{m_1, \\cdots, m_k\\}$. We minimize the total cluster variance with respect to the cluster means $\\{m_k\\}$ yielding the means of the currently assigned clusters.\n", "\n", "2. Given a current set of $k$ means $\\{m_k\\}$ the total cluster variance is minimized by assigning each observation to the closest (current) cluster mean. That is $$C(i) = \\underset{1\\leq k\\leq K}{\\mathrm{argmin}} ||\\boldsymbol{x_i} - \\boldsymbol{m_k}||^2$$\n", "\n", "3. Steps 1 and 2 are repeated until the assignments do not change.\n", "\n", "As previously stated the above formulation can be a bit difficult to understand,\n", "*at least the first time*, due to the dense notation used. But all in all the\n", "concept is fairly simple when explained in words. The math needs to be\n", "understood but to help you along the way we summarize the algorithm as follows\n", "(try to look at the terms above to match with the summary).\n", "\n", "1. Before we start we specify a number $k$ which is the number of clusters we want to try to separate our data into.\n", "\n", "2. We initially choose $k$ random data points in our data as our initial centroids, *or means* (this is where the name comes from).\n", "\n", "3. Assign each data point to their closest centroid, based on the squared Euclidean distance.\n", "\n", "4. For each of the $k$ cluster we update the centroid by calculating new mean values for all the data points in the cluster.\n", "\n", "5. Iteratively minimize the within cluster scatter by performing steps (3, 4) until the new assignments stop changing (can be to some tolerance) or until a maximum number of iterations have passed.\n", "\n", "That's it, nothing magical happening." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing Our Own Code\n", "In the following section we will work to develop a deeper understanding of the\n", "previously discussed mathematics through developing codes to do k-means cluster\n", "analysis." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Basic Python\n", "\n", "Let us now program the most basic version of the algorithm using nothing but\n", "Python with numpy arrays. This code is kept intentionally simple to gradually\n", "progress our understanding. There is no vectorization of any kind, and even most\n", "helper functions are not utilized. Throughout our implementation process it will\n", "be helpful to keep in mind both the mathematical description of the algorithm\n", "*and* our summary from above. In addition, try to think of ways to optimize this\n", "while reading the next section. We will get to it, take it as a challenge to see\n", "if your optimizations are better.\n", "\n", "First of all we need a dataset to do our cluster analysis on, for clarity (and\n", "lack of googling beforehand) we generate it ourselves using Gaussians. First we\n", "import" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import time\n", "from IPython.display import display\n", "\n", "np.random.seed(2021)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we define functions, for ease of use later, to generate Gaussians and to\n", "set up our toy data set." ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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