Abo Khedra, M. MMohammed, AmmarAbdel-Hamid, Yasser2023-09-282023-09-282023-0710.1109/IMSA58542.2023.10217368http://repository.msa.edu.eg/xmlui/handle/123456789/5727Process mining showed great capabilities in many fields, aiming to automatically extract the nature of process models from "event logs."Businesses commonly use process mining to improve key performance indicators (KPIs). Positive records in an event log are used instead of negative ones to achieve KPIs. Fitness, simplicity, accuracy, and generalization are the four primary quality forces for process models, and the current process discovery algorithms commonly take into account only a maximum of two of them. Thus, this paper introduces a novel two-phase approach. Phase one focuses on event log preprocessing by applying K-means clustering to divide the event logs into positive and negative groups according to established key performance indicators. The second phase addresses process discovery by balancing the four quality forces for the process model using the ETM process discovery algorithm. Using three publicly accessible real-life benchmark datasets, we run several experiments and measure the performance of the two-phase approach using the RapidProM workflow tool. The experimental findings reveal that the proposed two-phase approach model gets significant value from the negative records. The ETM process discovery algorithm performs well across the four primary quality forces.enETM Algorithm; Four Main Quality Dimension; K-means Clustering; Negative Event Log; Pre-processing Event Log; Process Discovery Algorithms; Process MiningArticle10.1109/IMSA58542.2023.10217368