Context Aware Trace Clustering: Towards Improving Process Mining Results.

Process Mining refers to the extraction of process models from event logs. Real-life processes tend to be less structured and more flexible. Traditional process mining algorithms have problems dealing with such unstructured processes and generate spaghetti-like process models that are hard to comprehend. An approach to overcome this is to cluster process instances (a process instance is manifested as a trace and an event log corresponds to a multi-set of traces) such that each of the resulting clusters correspond to a coherent set of process instances that can be adequately represented by a process model. In this paper, we propose a context aware approach to trace clustering based on generic edit distance. It is well known that the generic edit distance framework is highly sensitive to the costs of edit operations. We define an automated approach to derive the costs of edit operations. The method proposed in this paper outperforms contemporary approaches to trace clustering in proce...

• Type: Conference
• Year: 2009
• Where: SDM
• Authors: R. P. Jagadeesh Chandra Bose, Wil M. P. van der Aalst
• Keywords: Computer Science, Process Mining, Process Mining Algorithms, Process Models, SDM 2009