Is train/test-Split in unsupervised learning of neural network necessary? I am using autoencoder for anomaly detection in warranty data. It is unsupervised. I calculate the reconstruction error by the model and the records with high reconstruction error value is considered as an anomaly. I would like to know, if it is necessary to train/test-split the data.

Any help is much appreciated!
Yes it is still necessary, you are fitting your model on that data and learning it to find a good representation for that sample. Validating whether or not this was actually an anomaly is a lot more difficult then.