A trace clustering solution based on using the distance graph model

Process discovery is the most important task in the process mining. Because of the complexity of event logs (i.e. activities of several different processes are written into the same log), the discovered process models may be diffuse and unintelligible. That is why the input event logs should be clustered into simpler event sub-logs. This work provides a trace clustering solution based on the idea of using the distance graph model for trace representation. Experimental results proved the effect of the proposed solution on two measures of Fitness and Precision, especially the effect on the Precision measure.

Title: A trace clustering solution based on using the distance graph model
Authors: Ha, Q.-T.
Bui, H.-N.
Nguyen, T.-T.
Keywords: Distance graph model
Event log
Fitness measure
Precision measure
Process discovering
Process mining
Trace clustering
Issue Date: 2016
Publisher: Springer Verlag
Abstract: Process discovery is the most important task in the process mining. Because of the complexity of event logs (i.e. activities of several different processes are written into the same log), the discovered process models may be diffuse and unintelligible. That is why the input event logs should be clustered into simpler event sub-logs. This work provides a trace clustering solution based on the idea of using the distance graph model for trace representation. Experimental results proved the effect of the proposed solution on two measures of Fitness and Precision, especially the effect on the Precision measure.
Description: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Volume 9875, 2016, Pages 313-322
URI: http://link.springer.com/chapter/10.1007%2F978-3-319-45243-2_29
http://repository.vnu.edu.vn/handle/VNU_123/32321
ISSN: 03029743
Appears in Collections:Bài báo của ĐHQGHN trong Scopus

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