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I'm Josh Stormer, and welcome to Stat Quest, where we explore the exciting world of data analysis. Today, we're going to dive into k-means clustering and learn how to use it to identify patterns in our data.

Let's say you have a dataset that you want to divide into three clusters, such as measurements from three different types of tumors or cell types. While you might be able to identify these clusters by eye, it's more reliable to let a computer do the work for you. That's where k-means clustering comes in.

To start, you'll need to select the number of clusters you want to identify in your data, which is the "k" in k-means clustering. In this case, we'll select three clusters.

Next, you'll randomly select three distinct data points, which will serve as the initial clusters. Then, for each data point, you'll measure the distance between the point and the initial clusters. The point is then assigned to the nearest cluster.

Once all the points are in clusters, you'll calculate the mean of each cluster. Then, you'll repeat the process of measuring and clustering using the mean values. This process is repeated until the clustering no longer changes.

But how do you know if you've chosen the right number of clusters? One way to decide is to try different values for k and compare the total variation within each cluster. This is called an elbow plot, and you can pick k by finding the point at which the reduction in variation starts to level off.

K-means clustering is a powerful tool for identifying patterns in data, but it's important to remember that it's not always the best option. For example, if your data isn't plotted on a number line, you'll need to use the Euclidean distance in multiple dimensions. And if your data is a heatmap, you'll need to calculate the distances between things rather than plotting the data.

Despite these challenges, k-means clustering is a valuable tool for any data analyst. By following the steps outlined above, you'll be able to use it to identify patterns and gain insights from your data.

Thanks for joining me on this Stat Quest. Be sure to subscribe and hit the like button if you enjoyed this video. And if you want to support Stat Quest, consider buying one of my original songs. Tune in next time for another exciting Stat Quest!

Let's say you have a dataset that you want to divide into three clusters, such as measurements from three different types of tumors or cell types. While you might be able to identify these clusters by eye, it's more reliable to let a computer do the work for you. That's where k-means clustering comes in.

To start, you'll need to select the number of clusters you want to identify in your data, which is the "k" in k-means clustering. In this case, we'll select three clusters.

Next, you'll randomly select three distinct data points, which will serve as the initial clusters. Then, for each data point, you'll measure the distance between the point and the initial clusters. The point is then assigned to the nearest cluster.

Once all the points are in clusters, you'll calculate the mean of each cluster. Then, you'll repeat the process of measuring and clustering using the mean values. This process is repeated until the clustering no longer changes.

But how do you know if you've chosen the right number of clusters? One way to decide is to try different values for k and compare the total variation within each cluster. This is called an elbow plot, and you can pick k by finding the point at which the reduction in variation starts to level off.

K-means clustering is a powerful tool for identifying patterns in data, but it's important to remember that it's not always the best option. For example, if your data isn't plotted on a number line, you'll need to use the Euclidean distance in multiple dimensions. And if your data is a heatmap, you'll need to calculate the distances between things rather than plotting the data.

Despite these challenges, k-means clustering is a valuable tool for any data analyst. By following the steps outlined above, you'll be able to use it to identify patterns and gain insights from your data.

Thanks for joining me on this Stat Quest. Be sure to subscribe and hit the like button if you enjoyed this video. And if you want to support Stat Quest, consider buying one of my original songs. Tune in next time for another exciting Stat Quest!