# Discovering Business Area Effects to Process Mining Analysis Using Clustering and Influence Analysis

arXiv: https://arxiv.org/abs/2003.08170 · HTML: https://mlsanity.com/paper/2003.08170/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Mar 2020.

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

- [Clustering Object-Centric Event Logs](https://mlsanity.com/paper/2207.12764/) (2022)
- [Feature Recommendation for Structural Equation Model Discovery in Process Mining](https://mlsanity.com/paper/2108.07795/) (2021)

## Abstract (arXiv)

A common challenge for improving business processes in large organizations is that business people in charge of the operations are lacking a fact-based understanding of the execution details, process variants, and exceptions taking place in business operations. While existing process mining methodologies can discover these details based on event logs, it is challenging to communicate the process mining findings to business people. In this paper, we present a novel methodology for discovering business areas that have a significant effect on the process execution details. Our method uses clustering to group similar cases based on process flow characteristics and then influence analysis for detecting those business areas that correlate most with the discovered clusters. Our analysis serves as a bridge between BPM people and business, people facilitating the knowledge sharing between these groups. We also present an example analysis based on publicly available real-life purchase order process data.