  Novelty detection in discrete sequences is a challenging task, since
deviations from the process generating the normal data are often small or
intentionally hidden. Novelties can be detected by modeling normal sequences
and measuring the deviations of a new sequence from the model predictions.
However, in many applications data is generated by several distinct processes
so that models trained on all the data tend to over-generalize and novelties
remain undetected. We propose to approach this challenge through decomposition:
by clustering the data we break down the problem, obtaining simpler modeling
task in each cluster which can be modeled more accurately. However, this comes
at a trade-off, since the amount of training data per cluster is reduced. This
is a particular problem for discrete sequences where state-of-the-art models
are data-hungry. The success of this approach thus depends on the quality of
the clustering, i.e., whether the individual learning problems are sufficiently
simpler than the joint problem. While clustering discrete sequences
automatically is a challenging and domain-specific task, it is often easy for
human domain experts, given the right tools. In this paper, we adapt a
state-of-the-art visual analytics tool for discrete sequence clustering to
obtain informed clusters from domain experts and use LSTMs to model each
cluster individually. Our extensive empirical evaluation indicates that this
informed clustering outperforms automatic ones and that our approach
outperforms state-of-the-art novelty detection methods for discrete sequences
in three real-world application scenarios. In particular, decomposition
outperforms a global model despite less training data on each individual
cluster.
