How to Fill Color Regions in K-Means Clustering R

How to Fill Color Regions in K-Means Clustering R

Key points: R kmean clustering fill color region give us a better visualizing of the results K-means clustering is a more famous unsupervised machine learning technique that partitions data points into k groups based on similarity. A common way to visualize k-means clustering is to plot the data points in a scatter plot and color them according to their cluster membership, but this can be misleading or confusing if the clusters overlap or are not well separated. A better way to visualize in kmean clustering fill color region fill the regions of the plot with different colors corresponding to the clusters using the geom_mark_ellipse() function from the ggforce package. This function adds ellipses around groups of points based on their mean and covariance and allows us to map the cluster variable to the fill aesthetic using the aes() function. We can also add labels to the ellipses using the geom_text_repel() function from the ggrepel package, which repels overlapping text labels away from…