Over the course of this chapter, we dove into ML.NET's clustering support via the k-means clustering algorithm. We have also created and trained our first clustering application using k-means to predict what file type a file is. Lastly, we dove into how to evaluate a k-means clustering model and the various properties that ML.NET exposes to achieve a proper evaluation of a k-means clustering model.
In the next chapter, we will deep dive into anomaly detection algorithms with ML.NET by creating a login anomaly predictor.
