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Recent development in smart devices has lead us to an explosion in data generation and heterogeneity, which requires new network solutions for better analyzing and understanding traffic. These solutions should be intelligent and scalable in order to handle the huge amount of data automatically. With the progress of high-performance computing (HPC), it becomes feasible easily to deploy machine learning (ML) to solve complex problems and its efficiency has been validated in several domains (e.g., healthcare or computer vision). At the same time, network slicing (NS) has drawn significant attention from both industry and academia as it is essential to address the diversity of service requirements. Therefore, the adoption of ML within NS management is an interesting issue. In this paper, we have focused on analyzing network data with the objective of defining network slices according to traffic flow …