Discovering expressive process models by clustering log traces

By Gianluigi Greco, Antonella Guzzo, Luigi Pontieri, Domenico Saccà. IEEE Transactions on Knowledge and Data Engineering, 2006

360 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems, Management Information Systems

View Paper: https://www.semanticscholar.org/paper/ccbc8cc32648f07882cc12cf45e0c647ee95742b

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Wil M.P. van der Aalst
2. Boudewijn van Dongen
3. Minseok Song
4. A. Weijters
5. A. A. D. Medeiros
6. Christian W. Günther
7. A. Medeiros
8. Diogo R. Ferreira

In Information Systems

1. Wil M.P. van der Aalst
2. Boudewijn van Dongen
3. Minseok Song
4. A. Weijters
5. A. A. D. Medeiros
6. Christian W. Günther
7. Wil van der Aalst
8. Arya Adriansyah

In Management Information Systems

1. Wil M.P. van der Aalst
2. Boudewijn van Dongen
3. Minseok Song
4. A. Weijters
5. A. A. D. Medeiros
6. Christian W. Günther
7. Wil van der Aalst
8. A. Medeiros

Citations in the corpus

• Towards comprehensive support for organizational mining (2008, background): “The concept of conformance and several measures are proposed in [26,14]”
• Genetic process mining: an experimental evaluation (2007, background): “The approach by [26] handles non-free-choice situations by clustering the traces that follow different paths in the mined model.”
• Conformance Checking Using Cost-Based Fitness Analysis (2011, background): “There are already various metrics proposed related to the fitness dimension [1], [3], [5], [7], [8], [13].”
• Trace Clustering in Process Mining (2008, methodology): “[4] used trace clustering to discover expressive process models.”