K-Means Clustering Continued…
I went through a lab exercise today on k-means. I’ve put the code on my Github page here.
I remain in awe that you can fit a model with a few lines of code. A snippet is provided below. In this example, a k-means algorithm is run on data assigned to the variable ‘X’. How cool is that?
Here are some cool charts from the code on Github.
Key takeaways:
- K-means is an unsupervised machine learning technique to cluster data into a ‘k’ number of groups.
- Example use cases include customer segmentation, fraud detection, and grouping biological markers.
- It’s useful where the data isn’t labelled, and you want to explore and find patterns that might not be immediately obvious.