  Clustering is essential to many tasks in pattern recognition and computer
vision. With the advent of deep learning, there is an increasing interest in
learning deep unsupervised representations for clustering analysis. Many works
on this domain rely on variants of auto-encoders and use the encoder outputs as
representations/features for clustering. In this paper, we show that an l2
normalization constraint on these representations during auto-encoder training,
makes the representations more separable and compact in the Euclidean space
after training. This greatly improves the clustering accuracy when k-means
clustering is employed on the representations. We also propose a clustering
based unsupervised anomaly detection method using l2 normalized deep
auto-encoder representations. We show the effect of l2 normalization on anomaly
detection accuracy. We further show that the proposed anomaly detection method
greatly improves accuracy compared to previously proposed deep methods such as
reconstruction error based anomaly detection.
