Workflow mining: a survey of issues and approaches
Data & Knowledge Engineering
Process mining: a research agenda
Computers in Industry - Special issue: Process/workflow mining
Workflow Mining: Discovering Process Models from Event Logs
IEEE Transactions on Knowledge and Data Engineering
Reality mining: sensing complex social systems
Personal and Ubiquitous Computing
Discovering Expressive Process Models by Clustering Log Traces
IEEE Transactions on Knowledge and Data Engineering
Genetic process mining: an experimental evaluation
Data Mining and Knowledge Discovery
Finding Structure in Unstructured Processes: The Case for Process Mining
ACSD '07 Proceedings of the Seventh International Conference on Application of Concurrency to System Design
ProM 4.0: comprehensive support for real process analysis
ICATPN'07 Proceedings of the 28th international conference on Applications and theory of Petri nets and other models of concurrency
Approaching process mining with sequence clustering: experiments and findings
BPM'07 Proceedings of the 5th international conference on Business process management
Process mining based on clustering: a quest for precision
BPM'07 Proceedings of the 2007 international conference on Business process management
Conformance testing: measuring the fit and appropriateness of event logs and process models
BPM'05 Proceedings of the Third international conference on Business Process Management
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Process mining techniques attempt to extract non-trivial and useful information from event logs recorded by information systems. Process mining techniques have recently received notable attention in the literature for their ability to assist in the (re)design of complex processes by automatically discovering models that explain the events registered in some log traces provided as input. Real-life processes tend to be less structured and more flexible. An approach to overcome this is to cluster process instances such that each of the resulting clusters corresponds to coherent sets of process instances that can each be adequately represented by a process model. On the other hand the conformance checker methods check if model and the log conform to each other or not. This paper proposed an approach to use Appropriateness Conformance Checker methods to split the event log into homogeneous subsets and for each subset a process model is created. To illustrate this we present a real-life case study from reality mining dataset provided by MIT (Massachusetts Institute of Technology) Media Laboratory. The whole approach has been implemented in ProM the process mining framework.