# Clustering Object-Centric Event Logs

arXiv: https://arxiv.org/abs/2207.12764 · HTML: https://mlsanity.com/paper/2207.12764/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 32 references that are in the map.
- First posted on arXiv 26 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [Process Mining for Python (PM4Py): Bridging the Gap Between Process- and Data Science](https://mlsanity.com/paper/1905.06169/) (2019)
- [Clustering-Based Predictive Process Monitoring](https://mlsanity.com/paper/1506.01428/) (2015)
- [Multi-Dimensional Event Data in Graph Databases](https://mlsanity.com/paper/2005.14552/) (2020)
- [Comprehensive Process Drift Detection with Visual Analytics](https://mlsanity.com/paper/1907.06386/) (2019)
- [Extracting Multiple Viewpoint Models from Relational Databases](https://mlsanity.com/paper/2001.02562/) (2020)
- [Causal Process Mining from Relational Databases with Domain Knowledge](https://mlsanity.com/paper/2202.08314/) (2022)
- [Process Comparison Using Object-Centric Process Cubes](https://mlsanity.com/paper/2103.07184/) (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)
- [Expert-driven Trace Clustering with Instance-level Constraints](https://mlsanity.com/paper/2110.06703/) (2021)
- [Object Type Clustering using Markov Directly-Follow Multigraph in Object-Centric Process Mining](https://mlsanity.com/paper/2206.11017/) (2022)
- [Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis](https://mlsanity.com/paper/2003.08170/) (2020)
- [Filtering and Sampling Object-Centric Event Logs](https://mlsanity.com/paper/2205.01428/) (2022)

## Built on by (most cited first, 2 of 2)

- [Extracting Process-Aware Decision Models from Object-Centric Process Data](https://mlsanity.com/paper/2401.14847/) (2024)
- [Credit Card Fraud Detection Using Asexual Reproduction Optimization](https://mlsanity.com/paper/2306.01008/) (2023)

## Abstract (arXiv)

Process mining provides various algorithms to analyze process executions based on event data. Process discovery, the most prominent category of process mining techniques, aims to discover process models from event logs, however, it leads to spaghetti models when working with real-life data. Therefore, several clustering techniques have been proposed on top of traditional event logs (i.e., event logs with a single case notion) to reduce the complexity of process models and discover homogeneous subsets of cases. Nevertheless, in real-life processes, particularly in the context of Business-to-Business (B2B) processes, multiple objects are involved in a process. Recently, Object-Centric Event Logs (OCELs) have been introduced to capture the information of such processes, and several process discovery techniques have been developed on top of OCELs. Yet, the output of the proposed discovery techniques on real OCELs leads to more informative but also more complex models. In this paper, we propose a clustering-based approach to cluster similar objects in OCELs to simplify the obtained process models. Using a case study of a real B2B process, we demonstrate that our approach reduces the complexity of the process models and generates coherent subsets of objects which help the end-users gain insights into the process.