What is k-means visualization and how does it work?

What is k-means visualization and how does it work?

K-means visualization is a technique used to represent the results of the k-means clustering algorithm, which groups data into k clusters based on their features.

Can you explain how the k-means algorithm actually clusters the data?

Sure! The k-means algorithm starts by selecting k initial centroids, then assigns each data point to the nearest centroid, and finally updates the centroids based on the mean of the assigned points. This process repeats until the centroids stabilize.

How can I visualize the clusters formed by k-means?

You can visualize clusters using scatter plots, where each point represents a data point and is colored based on its assigned cluster. Libraries like Matplotlib in Python can help with this.

Are there any limitations to k-means clustering?

Yes, k-means assumes spherical clusters and is sensitive to outliers. It also requires the number of clusters to be specified in advance.