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Information Technology Journal
  Year: 2011 | Volume: 10 | Issue: 6 | Page No.: 1240-1245
DOI: 10.3923/itj.2011.1240.1245
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An Efficient Process Mining Method Based on Discrete Particle Swarm Optimization

Xianwen Fang, Xin Gao, Zhixiang Yin and Qianjin Zhao

Process mining is to extract business process models from event logs, the mining process is an important learning task. However, the discovery of these processes poses many challenges, including noise, non-local, non-free choice constructs and so on. In the study, we give out the definition of the behavior redundancy degree which is benefit to analyze the behavior conformance. Then, in order to build the optimal the process model, a process mining method based on Discrete Particle Swarm Optimization (DPSO) is presented. The method can take into account the basic Petri net structure and the metrics of behavior conformance and avoid the blindness of building process model. Finally, a DPSO process mining plug-in is developed and a number of event log is tested in the DPSO mining plug-in based on PROM platform. Theoretical analysis and experimental results show that DPSO-based mining method has better behavior fitness and behavior appropriateness in business process mining.
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  •    An Optimized Method of Business Process Mining Based on the Behavior Profile of Petri Nets
  •    A Layout Pattern Based Particle Swarm Optimization for Constrained Packing Problems
  •    Influence Analysis and Evaluation of Wheel Parameters on Motion Performance of Lunar Rover
  •    Multi-objective Optimization using Chaos Based PSO
How to cite this article:

Xianwen Fang, Xin Gao, Zhixiang Yin and Qianjin Zhao, 2011. An Efficient Process Mining Method Based on Discrete Particle Swarm Optimization. Information Technology Journal, 10: 1240-1245.

DOI: 10.3923/itj.2011.1240.1245






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