Two-phase clustering process for outliers detection
Pattern Recognition Letters
Findout: finding outliers in very large datasets
Knowledge and Information Systems
Handbook of data mining and knowledge discovery
Information-theoretic co-clustering
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Mining of ad-hoc business processes with TeamLog
Data & Knowledge Engineering
A Rule-Based Approach for Process Discovery: Dealing with Noise and Imbalance in Process Logs
Data Mining and Knowledge Discovery
A clustering-based method for unsupervised intrusion detections
Pattern Recognition Letters
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Process diagnostics using trace alignment: Opportunities, issues, and challenges
Information Systems
Projection approaches to process mining using region-based techniques
Data Mining and Knowledge Discovery
Process mining in healthcare: data challenges when answering frequently posed questions
BPM' 2012 Proceedings of the 2012 international conference on Process Support and Knowledge Representation in Health Care
Multidimensional process mining: a flexible analysis approach for health services research
Proceedings of the Joint EDBT/ICDT 2013 Workshops
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Classical outlier detection approaches may hardly fit process mining applications, since in these settings anomalies emerge not only as deviations from the sequence of events most often registered in the log, but also as deviations from the behavior prescribed by some (possibly unknown) process model. These issues have been faced in the paper via an approach for singling out anomalous evolutions within a set of process traces, which takes into account both statistical properties of the log and the constraints associated with the process model. The approach combines the discovery of frequent execution patterns with a cluster-based anomaly detection procedure; notably, this procedure is suited to deal with categorical data and is, hence, interesting in its own, given that outlier detection has mainly been studied on numerical domains in the literature. All the algorithms presented in the paper have been implemented and integrated into a system prototype that has been thoroughly tested to assess its scalability and effectiveness.