I recently learnt about the anamoly detection using autoencoders(specifically denoisinng autoencoders).To train the autoencoders do we need a data having some pattern? or is there some way to train in abnormal data ?Also how we decide that the data is suitable for training autoencoder model.
You need normal data to train on. If you have abnormal instances also, those should be excluded from the training set. Having access to labeled abnormal/normal data is very useful for the validation and testset. Anything that differs from the normal data (as learned by the autoencoder) is considered an anomaly.
If you have a lot of labeled abnormal and normal data, then you can consider using binary classification instead.