ICMA: a new efficient algorithm for process model discovery

Nowadays, Business processes in organizations are supported by information systems. These systems record organizational processes outputs in the form of event logs, which contain valuable information about processes and their performance. Process mining extract knowledge from event logs. One of the most important tasks in process mining is process model discovery that uses an algorithm to build a process model from a given event log. In this research, a new model which named ICMA proposed for discovering process models. This model has three steps, pre-processing phase, body of model and post-processing phase. Imperialist Competitive Algorithm (ICA) was used for the first time as body of proposed model. Nine hundred nineteen event logs were used, those are balanced event logs, unbalanced event logs, and real-life event logs. Moreover, those event logs were studied at the 0%, 1%, 5%, 10% and 20% noise levels and the results have compared to the recent Vazquise algorithm. The research findings revealed that precision and completeness of our model is better than Vazquez model. In this paper has been shown that the ICMA model compared to the other approaches in literature method drastically improved the precision and completeness of the model. In addition, the noise problem was satisfactorily solved through data pre-processing and post-processing operations.