So in R, the kmeans function is the function that we use to it, to implement the kmeans algorithm, here. And you can a, I, you can dem, I can demonstrate it here with use, using a simple data frame. I got two, with just two dimensions. And I called kmeans on the data frame and I tell it there are three centers. Alright, so three centroids and what kmeans returns is a, is a list with a number of different elements in it. And so, for example, the, probably the most important element is the cluster element and you can see here when I print out the cluster element, you can see that it's a vector of numbers from one to three so. And what this shows is that for each data point in the in the data frame that I passed it, it tells me which cluster it's in. So you can see that the first four points are in cluster three, the next four are in cluster one, and the next four are in cluster two. You can see another element if you look in the printout of the names another element of the, of the object returned from kmeans is called centers. And this tells you the location of the centroids in the space. So, if you want to plot the kind of, the, the results for kmeans, the first thing you can do is you can run the kmeans algorithm on your data. And here, I'm just going to plot the data. So the first thing I do is I plot the data, so I plot X Y and then I color the data points according to the cluster that they happen to be in. So you can see I, I pass the color argument to be equal to the cluster number. And then I, I use the points function to kind of add the centers, the clusters centroids to the plot and I, and I plot them using the plus symbol. So, here I plotted the, the data and the kmeans clustering results. Finally, another way that you can visualize clustering information for an outcome in an algorithm like kmeans is by using the Heatmap function or, or use, looking at heatmaps I should say. So here I've just, I've, I've using the same data I've taken out a different random sample of the data set. I sampled its replacement and I just run kmeans again, again with three centers. And I stored it in an object called kmeans object two. And now I'm going to make a, an image plot of the data, so the first plot on the left here is just an image of the original data. And then on the right hand side I've reordered the the columns of the data, I'm sorry, I should say the rows of the data frame so, so that the clusters are kind of put together. So here, you can see that if you go up and down the, up and down this matrix. You'll see the cluster, the, the data points are clustered together so that they are next to each other. And so you can use this to look at high dimensional data, and high dimensional image type data, or matrix type data where you can reorganize the rows and the columns and kind of look at clusters that are closer together or farther apart. and, and, and, or, kind of, and in it, and so look at your kind of matrix data in an organized way so you can look for, so you can look for patterns. We'll talk a little bit about this more when we talk about hierarchical clustering, but again, you can, you can use heatmap type of visualizations with other types of clustering algorithms like kmeans too. So it's, just to summarize, you know, kmeans is a handy algorithm for organizing and looking for patterns that hide eventual data. A couple of things that I for, it requires that you know the number of clusters. So you have to specify at least roughly speaking, how many clusters there are. You can, you can kind of play with that a little bit to determine, to figure out kind of what, what pattern probably looks the best, but there's no easy rule there. And then so you have to pick those clusters out by eye or sort of through some other mechanism. There are a few algorithms for kind of determining the number of clusters using, either using cross-validation, information theory, other types of metrics. And so there's, here's a link to determining the number of clusters. And it's, and the kmeans algorithm is not deterministic, so there are, depending on how it's implemented, there can be, sometimes those starting points are chosen at random, and so, so it's often useful to run the kmeans algorithm a couple times just to make sure you're not getting a very unstable finishing point. So, for example, if you run it three different times, and every time you get a totally different pattern, then that means that the, the algorithm may not be, have a very stable kind of view of the data. And so, so, kmeans is, can, can be problematic in that way for certain types of datasets. And here, I've got a couple links to kind of, to videos and, and references on, that, that provide a lot more information about kmeans.
