  We suggest a novel method of clustering and exploratory analysis of temporal
event sequences data (also known as categorical time series) based on
three-dimensional data grid models. A data set of temporal event sequences can
be represented as a data set of three-dimensional points, each point is defined
by three variables: a sequence identifier, a time value and an event value.
Instantiating data grid models to the 3D-points turns the problem into
3D-coclustering.
  The sequences are partitioned into clusters, the time variable is discretized
into intervals and the events are partitioned into clusters. The cross-product
of the univariate partitions forms a multivariate partition of the
representation space, i.e., a grid of cells and it also represents a
nonparametric estimator of the joint distribution of the sequences, time and
events dimensions. Thus, the sequences are grouped together because they have
similar joint distribution of time and events, i.e., similar distribution of
events along the time dimension. The best data grid is computed using a
parameter-free Bayesian model selection approach. We also suggest several
criteria for exploiting the resulting grid through agglomerative hierarchies,
for interpreting the clusters of sequences and characterizing their components
through insightful visualizations. Extensive experiments on both synthetic and
real-world data sets demonstrate that data grid models are efficient, effective
and discover meaningful underlying patterns of categorical time series data.
