From the course: Data Science Foundations: Data Mining in R

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K-means

K-means

From the course: Data Science Foundations: Data Mining in R

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K-means

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- [Instructor] In our last demonstration, we looked at hierarchical clustering, which is great when you want to look at as many different ways of clustering as possible. It goes from one cluster for every single case, or it puts each case in its own individual cluster, and you can look at any variation in between. On the other hand, there are times when you know in advance how many clusters you want. It might be either 'cause you know, for instance, you're dealing with three species, or it might be that you're doing audience segmentation in marketing, but you know you only have enough time or money for three different campaigns, and so you have to limit the number of different clusters you're dealing with. In that situation, K-means is a great way where K is a number of groups that you want, and then you put each group around the multidimensional means. Let's take a look at this with the penguins dataset that we did…

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