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• W2896831214 abstract "One of the challenges in applying process mining algorithms on real event data, is the presence of outlier behavior. Such behaviour often leads to complex, incomprehensible, and, sometimes, even inaccurate process mining results. As a result, correct and/or important behaviour of the process may be concealed. In this paper, we exploit sequence mining techniques for the purpose of outlier detection in the process mining domain. Using the proposed approach, it is even possible to detect outliers in case of heavy parallelism and/or long-term dependencies between business process activities. Our method has been implemented in both the ProM- and the RapidProM framework. Using these implementations, we conducted a collection of experiments that show that we are able to detect and remove outlier behaviour in event data. Our evaluation clearly demonstrates that the proposed method accurately removes outlier behaviour and, indeed, improves process discovery results." @default.
• W2896831214 created "2018-10-26" @default.
• W2896831214 creator A5041157578 @default.
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• W2896831214 date "2018-01-01" @default.
• W2896831214 modified "2023-10-02" @default.
• W2896831214 title "Applying Sequence Mining for Outlier Detection in Process Mining" @default.
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• W2896831214 doi "https://doi.org/10.1007/978-3-030-02671-4_6" @default.
• W2896831214 hasPublicationYear "2018" @default.
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