Process mining based on clustering: A quest for precision

A.K. Alves De Medeiros, A. Guzzo, G. Greco, W.M.P. Aalst, van der, A.J.M.M. Weijters, B.F. Dongen, van, D. Saccà

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademic

86 Citations (Scopus)


Process mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. For example, there are many process mining techniques to automatically discover a process model based on some event log. Most of these algorithms perform well on structured processes with little disturbances. However, in reality it is difficult to determine the scope of a process and typically there are all kinds of disturbances. As a result, process mining techniques produce spaghetti-like models that are difficult to read and that attempt to merge unrelated cases. To address these problems, we use an approach where the event log is clustered iteratively such that each of the resulting clusters corresponds to a coherent set of cases that can be adequately represented by a process model. The approach allows for different clustering and process discovery algorithms. In this paper, we provide a particular clustering algorithm that avoids over-generalization and a process discovery algorithm that is much more robust than the algorithms described in literature [1]. The whole approach has been implemented in ProM.
Original languageEnglish
Title of host publicationBusiness Process Management Workshops (BPM 2007 International Workshops, BPI, BPD, CBP, ProHealth, RefMod, semantics4ws, Brisbane, Australia, September 24, 2007, Revised Selected Papers)
EditorsA.H.M. Hofstede, ter, B. Benatallah, H.Y. Paik
Place of PublicationBerlin
Number of pages13
ISBN (Print)978-3-540-78237-7
Publication statusPublished - 2008

Publication series

NameLecture Notes in Computer Science
ISSN (Print)0302-9743


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