Supercomputer is a fundamental means to perform complex and huge computations. Simultaneously, it is one of the most energy consuming infrastructures. The diversity of applications executed within a HPC system makes it difficult to control resource utilization and identify the behaviour of these applications while running. To effectively alleviate this concern, scientists have appealed to use machine learning techniques for HPC monitoring and diagnosis. This work focuses on the employment of Gath-Geva clustering algorithm to identify applications and their behavioural similarities while running on HPC system. The choice of this algorithm is based on its ability to be adapted to data structures of arbitrarily shaped, sized and dense data.