  We propose a new type of variational autoencoder to perform improved
pre-processing for clustering and anomaly detection on data with a given label.
Anomalies however are not known or labeled. We call our method conditional
latent space variational autonencoder since it separates the latent space by
conditioning on information within the data. The method fits one prior
distribution to each class in the dataset, effectively expanding the prior
distribution to include a Gaussian mixture model. Our approach is compared
against the capabilities of a typical variational autoencoder by measuring
their V-score during cluster formation with respect to the k-means and EM
algorithms.
  For anomaly detection, we use a new metric composed of the mass-volume and
excess-mass curves which can work in an unsupervised setting. We compare the
results between established methods such as as isolation forest, local outlier
factor and one-class support vector machine.
