because we build models at scale with that so if you want to build it you can use the Cagle data set but we also chose it because of that then just the difference with k-means and an autoencoder using an anomaly detection problem so we have used k-means before and you'd find the distance between the chester centroid and the observation does show anomalies but maintaining that code so when you have to retrain your model with new data your cluster sense has change your clusters change inevitably what you are trying to find