→ A clustering algorithm looks at a number of data points and automatically finds data points that are related or similar to each other.
⇒ In unsupervised learning, you are given a dataset like this with just x, but not the labels or the target labels y.
Because we don't have target labels y, we're not able to tell the algorithm what is the "right answer, y" that we wanted to predict.
Instead, we're going to ask the algorithm to find something interesting about the data, that is to find some interesting structure about this data.
⇒ The first unsupervised learning algorithm that you learn about is called a clustering algorithm, which looks for one particular type of structure in the data. Namely, look at the dataset like this and try to see if it can be grouped into clusters.
<aside> 💡 Clusters → groups of points that are similar to each other.
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<aside> 💡 One of the applications I found fascinating was astronomers using clustering to group bodies together to figure out which ones form one galaxy or which one form coherent structures in space.
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