A generic framework for trace clustering in process mining

Zandkarimi, Fareed ; Rehse, Jana-Rebecca ; Soudmand, Pouya ; Höhle, Hartmut

DOI: https://doi.org/10.1109/ICPM49681.2020.00034
URL: https://ieeexplore.ieee.org/document/9230304
Document Type: Conference or workshop publication
Year of publication: 2020
Book title: 2nd International Conference on Process Mining (ICPM) : Virtual conference, 4-9 October 2020, Padua, Italy, proceedings
The title of a journal, publication series: 2020 2nd International Conference on Process Mining (ICPM)
Page range: 177-184
Conference title: ICPM 2020
Location of the conference venue: Online
Date of the conference: 05.-08.10.2020
Publisher: Dongen, Boudewijn van
Place of publication: Los Alamitos, CA [u.a.]
Publishing house: IEEE Computer Society
ISBN: 978-1-7281-9833-0 , 978-1-72-819832-3
Publication language: English
Institution: Business School > Enterprise Systems (Höhle 2017-)
Subject: 004 Computer science, internet
330 Economics
Abstract: The goal of process discovery is to visualize event log data as a process model. In reality, however, these models are often highly complex. Process trace clustering is a well-studied and powerful technique to address this. It groups an event log into more cohesive sub logs, such that the discovered process models become less complex and easier to understand. Over the past 15 years, researchers proposed various approaches for trace clustering in process discovery. The developed approaches vary greatly with regard to algorithmic capacities, data characteristics, computational complexity, and integration of additional information. In this paper, we provide a state-of-the-art analysis of trace clustering by a) performing a systematic literature review, and b) proposing a generic framework for trace clustering. Eventually, our goal is to provide an overview of current trace clustering research and a basis for developing new methods and approaches to trace clustering.

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