  There is emerging attention towards working with event sequences. In
particular, clustering of event sequences is widely applicable in domains such
as healthcare, marketing, and finance. Use cases include analysis of visitors
to websites, hospitals, or bank transactions. Unlike traditional time series,
event sequences tend to be sparse and not equally spaced in time. As a result,
they exhibit different properties, which are essential to account for when
developing state-of-the-art methods.
  The community has paid little attention to the specifics of heterogeneous
event sequences. Existing research in clustering primarily focuses on classic
times series data. It is unclear if proposed methods in the literature
generalize well to event sequences.
  Here we propose COHORTNEY as a novel deep learning method for clustering
heterogeneous event sequences. Our contributions include (i) a novel method
using a combination of LSTM and the EM algorithm and code implementation; (ii)
a comparison of this method to previous research on time series and event
sequence clustering; (iii) a performance benchmark of different approaches on a
new dataset from the finance industry and fourteen additional datasets. Our
results show that COHORTNEY vastly outperforms in speed and cluster quality the
state-of-the-art algorithm for clustering event sequences.
