What is K-means clustering and how does it work?

What is K-means clustering and how does it work?

K-means clustering is a method used to partition data into distinct groups based on their features. It works by initializing 'k' centroids, assigning data points to the nearest centroid, and then updating the centroids based on the assigned points. This process repeats until the centroids stabilize.

What are some practical applications of K-means clustering?

K-means clustering is widely used in market segmentation, image compression, and organizing computing clusters. It's also useful in customer segmentation for targeted marketing.

How do I choose the right value for 'k'?

Choosing the right 'k' can be done using methods like the Elbow Method, where you plot the explained variance against the number of clusters and look for an 'elbow' point.

Are there any limitations to K-means clustering?

Yes, K-means assumes spherical clusters and equal sizes, which may not always be the case. It can also be sensitive to outliers.

Can K-means clustering be used for non-numerical data?

K-means is primarily designed for numerical data. However, you can use techniques like one-hot encoding to convert categorical data into a numerical format before applying K-means.