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Now I want to show you a visualization tool that I found online that I

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think does a really great job of helping you see what k-means clustering does.

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And that should give you a good intuition for how it works.

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So I'd like to give a special shout out to Naftali Harris,

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who wrote this visualization and very kindly agreed to let us use it.

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I'll put a link to this website in the instructor notes that you can go and

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play around with it on your own.

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So it starts out by asking me how to pick the initial centroids of my clusters.

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I'll start out with Randomly right now.

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What kind of data would I like to use?

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There are a number of different things here, and

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I encourage you to play around with them.

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A Gaussian Mixture has been really similar to one of the simple examples we've

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done so far.

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So Gaussian mixture data looks like this.

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These are all the points that we have to classify.

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The first question for you is,

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how many centroids do you think is the correct number of centroids on this data?