# Learning Data Science: Understanding and Using k-means Clustering

Posted on July 23, 2019 by Learning Machines in R bloggers | 0 Comments

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A few months ago I published a quite popular post on Clustering the Bible… one well known

*clustering*algorithm is

*k-means*. If you want to learn how

*k*-means works and how to apply it in a real-world example, read on…

*k*-means (not to be confused with *k-nearest neighbours* or *KNN*: Teach R to read handwritten Digits with just 4 Lines of Code) is a simple, yet often very effective *unsupervised learning* algorithm to find similarities in large amounts of data and cluster them accordingly. The *k* stands for the number of clusters which has to be set beforehand.

The guiding principles are:

- The
*distance*between data points within clusters should be as small as possible. - The distance of the
*centroids*(= centre of the clusters) should be as big as possible.

Because there are too many possible combinations of all possible clusters comprising all possible data points *k*-means follows an *iterative* approach:

- Initialization: assign clusters randomly to all data points
- E-step (for expectation): assign each observation to the “nearest” (based on
*Euclidean distance*) cluster - M-step (for maximization): determine new centroids based on the mean of assigned objects
- Repeat steps 3 and 4 until no further changes occur

As can be seen above *k*-means is an example of a so-called *expectation-maximization algorithm*.

To implement *k*-means in R we first assign some variables and define a helper function for plotting the steps:

n <- 3 # no. of centroids set.seed(1415) # set seed for reproducibility M1 <- matrix(round(runif(100, 1, 5), 1), ncol = 2) M2 <- matrix(round(runif(100, 7, 12), 1), ncol = 2) M3 <- matrix(round(runif(100, 20, 25), 1), ncol = 2) M <- rbind(M1, M2, M3) C <- M[1:n, ] # define centroids as first n objects obs <- length(M) / 2 A <- sample(1:n, obs, replace = TRUE) # assign objects to centroids at random colors <- seq(10, 200, 25) clusterplot <- function(M, C, txt) { plot(M, main = txt, xlab = "", ylab = "") for(i in 1:n) { points(C[i, , drop = FALSE], pch = 23, lwd = 3, col = colors[i]) points(M[A == i, , drop = FALSE], col = colors[i]) } } clusterplot(M, C, "Initialization")

Here comes the *k*-means algorithm as described above:

repeat { # calculate Euclidean distance between objects and centroids D <- matrix(data = NA, nrow = n, ncol = obs) for(i in 1:n) { for(j in 1:obs) { D[i, j] <- sqrt((M[j, 1] - C[i, 1])^2 + (M[j, 2] - C[i, 2])^2) } } O <- A ## E-step: parameters are fixed, distributions are optimized A <- max.col(t(-D)) # assign objects to centroids if(all(O == A)) break # if no change stop clusterplot(M, C, "E-step") ## M-step: distributions are fixed, parameters are optimized # determine new centroids based on mean of assigned objects for(i in 1:n) { C[i, ] <- apply(M[A == i, , drop = FALSE], 2, mean) } clusterplot(M, C, "M-step") }

As can seen the clusters wander slowly but surely until all three are stable. We now compare the result with the `k-means`

function in Base R:

cl <- kmeans(M, n) clusterplot(M, cl$centers, "Base R")

(custom <- C[order(C[ , 1]), ]) ## [,1] [,2] ## [1,] 3.008 2.740 ## [2,] 9.518 9.326 ## [3,] 22.754 22.396 (base <- cl$centers[order(cl$centers[ , 1]), ]) ## [,1] [,2] ## 2 3.008 2.740 ## 1 9.518 9.326 ## 3 22.754 22.396 round(base - custom, 13) ## [,1] [,2] ## 2 0 0 ## 1 0 0 ## 3 0 0

As you can see, the result is the same!

Now, for some real-world application: clustering wholesale customer data. The data set refers to clients of a wholesale distributor. It includes the annual spending on diverse product categories and is from the renowned *UCI Machine Learning Repository* (I guess the category “Delicassen” should rather be “Delicatessen”).

Have a look at the following code:

data <- read.csv("https://archive.ics.uci.edu/ml/machine-learning-databases/00292/Wholesale customers data.csv", header = TRUE) head(data) ## Channel Region Fresh Milk Grocery Frozen Detergents_Paper Delicassen ## 1 2 3 12669 9656 7561 214 2674 1338 ## 2 2 3 7057 9810 9568 1762 3293 1776 ## 3 2 3 6353 8808 7684 2405 3516 7844 ## 4 1 3 13265 1196 4221 6404 507 1788 ## 5 2 3 22615 5410 7198 3915 1777 5185 ## 6 2 3 9413 8259 5126 666 1795 1451 set.seed(123) k <- kmeans(data[ , -c(1, 2)], centers = 4) # remove columns 1 and 2, create 4 clusters (centers <- k$centers) # display cluster centers ## Fresh Milk Grocery Frozen Detergents_Paper Delicassen ## 1 8149.837 18715.857 27756.592 2034.714 12523.020 2282.143 ## 2 20598.389 3789.425 5027.274 3993.540 1120.142 1638.398 ## 3 48777.375 6607.375 6197.792 9462.792 932.125 4435.333 ## 4 5442.969 4120.071 5597.087 2258.157 1989.299 1053.272 round(prop.table(centers, 2) * 100) # percentage of sales per category ## Fresh Milk Grocery Frozen Detergents_Paper Delicassen ## 1 10 56 62 11 76 24 ## 2 25 11 11 22 7 17 ## 3 59 20 14 53 6 47 ## 4 7 12 13 13 12 11 table(k$cluster) # number of customers per cluster ## ## 1 2 3 4 ## 49 113 24 254

One interpretation could be the following for the four clusters:

- Big general shops
- Small food shops
- Big food shops
- Small general shops

As you can see, the interpretation of some clusters found by the algorithm can be quite a challenge. If you have a better idea of how to interpret the result please tell me in the comments below!

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