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Title: Active trace clustering for improved process discovery
Authors: De Weerdt, Jochen ×
vanden Broucke, Seppe
Vanthienen, Jan
Baesens, Bart #
Issue Date: 2013
Publisher: Institute of Electrical and Electronics Engineers
Series Title: IEEE Transactions on Knowledge and Data Engineering vol:25 issue:12 pages:2708-2720
Abstract: Process discovery is the learning task that entails the construction of process models from event logs of nformation
systems. Typically, these event logs are large data sets that contain the process executions by registering what activity has taken place at a certain moment in time. By far the most arduous challenge for process discovery algorithms consists of tackling the problem of accurate and comprehensible knowledge discovery from highly flexible environments. Event logs from such flexible systems often contain a large variety of process executions which makes the application of Process Mining most interesting.
However, simply applying existing process discovery techniques will often yield highly incomprehensible process models because of their inaccuracy and complexity.With respect to resolving this problem, trace clustering is one very interesting approach since it allows to split up an existing event log so as to facilitate the knowledge discovery process. In this paper, we propose a novel trace
clustering technique that significantly differs from previous approaches. Above all, it starts from the observation that currently available techniques suffer from a large divergence between the clustering bias and the evaluation bias. By employing an active learning inspired approach, this bias divergence is solved. In an assessment using four complex, real-life event logs, it is shown
that our technique significantly outperforms currently available trace clustering techniques.
ISSN: 1041-4347
Publication status: published
KU Leuven publication type: IT
Appears in Collections:Research Center for Management Informatics (LIRIS) @ Leuven
× corresponding author
# (joint) last author

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