  Trace clustering has increasingly been applied to find homogenous process
executions. However, current techniques have difficulties in finding a
meaningful and insightful clustering of patients on the basis of healthcare
data. The resulting clusters are often not in line with those of medical
experts, nor do the clusters guarantee to help return meaningful process maps
of patients' clinical pathways. After all, a single hospital may conduct
thousands of distinct activities and generate millions of events per year. In
this paper, we propose a novel trace clustering approach by using sample sets
of patients provided by medical experts. More specifically, we learn frequent
sequence patterns on a sample set, rank each patient based on the patterns, and
use an automated approach to determine the corresponding cluster. We find each
cluster separately, while the frequent sequence patterns are used to discover a
process map. The approach is implemented in ProM and evaluated using a large
data set obtained from a university medical center. The evaluation shows
F1-scores of 0.7 for grouping kidney injury, 0.9 for diabetes, and 0.64 for
head/neck tumor, while the process maps show meaningful behavioral patterns of
the clinical pathways of these groups, according to the domain experts.

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