  Object-centric process mining is a new paradigm with more realistic
assumptions about underlying data by considering several case notions, e.g., an
order handling process can be analyzed based on order, item, package, and route
case notions. Including many case notions can result in a very complex model.
To cope with such complexity, this paper introduces a new approach to cluster
similar case notions based on Markov Directly-Follow Multigraph, which is an
extended version of the well-known Directly-Follow Graph supported by many
industrial and academic process mining tools. This graph is used to calculate a
similarity matrix for discovering clusters of similar case notions based on a
threshold. A threshold tuning algorithm is also defined to identify sets of
different clusters that can be discovered based on different levels of
similarity. Thus, the cluster discovery will not rely on merely analysts'
assumptions. The approach is implemented and released as a part of a python
library, called processmining, and it is evaluated through a Purchase to Pay
(P2P) object-centric event log file. Some discovered clusters are evaluated by
discovering Directly Follow-Multigraph by flattening the log based on the
clusters. The similarity between identified clusters is also evaluated by
calculating the similarity between the behavior of the process models
discovered for each case notion using inductive miner based on footprints
conformance checking.
