# Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining

arXiv: https://arxiv.org/abs/2206.11017 · HTML: https://mlsanity.com/paper/2206.11017/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 28 references that are in the map.
- First posted on arXiv 22 Jun 2022.

## Builds on (most cited first, 9 of 9)

- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [Discovering Object-Centric Petri Nets](https://mlsanity.com/paper/2010.02047/) (2020)
- [Multi-Dimensional Event Data in Graph Databases](https://mlsanity.com/paper/2005.14552/) (2020)
- [Precision and Fitness in Object-Centric Process Mining](https://mlsanity.com/paper/2110.05375/) (2021)
- [A Scalable Database for the Storage of Object-Centric Event Logs](https://mlsanity.com/paper/2202.05639/) (2022)
- [A Python Tool for Object-Centric Process Mining Comparison](https://mlsanity.com/paper/2202.05709/) (2022)
- [An Event Data Extraction Approach from SAP ERP for Process Mining](https://mlsanity.com/paper/2110.03467/) (2021)
- [Graph-based process mining](https://mlsanity.com/paper/2007.09352/) (2020)
- [Business Process Variant Analysis based on Mutual Fingerprints of Event Logs](https://mlsanity.com/paper/1912.10598/) (2019)

## Built on by (most cited first, 3 of 3)

- [AI-Enhanced Business Process Automation: A Case Study in the Insurance Domain Using Object-Centric Process Mining](https://mlsanity.com/paper/2504.17295/) (2025)
- [Clustering Object-Centric Event Logs](https://mlsanity.com/paper/2207.12764/) (2022)
- [OCPM2: Extending the Process Mining Methodology for Object-Centric Event Data Extraction](https://mlsanity.com/paper/2503.10735/) (2025)

## Abstract (arXiv)

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.