<p>Gradual patterns translate co-variations of the numerical attributes of transactional databases. They play a crucial role in many real-world applications where there is a large amount of digital data to manage. This type of patterns has attracted attention of the data mining community, and several algorithms have been designed to extract frequent gradual patterns from transactional databases. The algorithms for extracting frequent gradual patterns in large databases are CPU and memory intensive, which poses the problem of improving their performance. This paper proposes a novel approach to improve the performance of frequent gradual pattern mining algorithms. It relies on the design of a gradual support inference graph to avoid redundancies in the calculations of gradual supports and to bypass the calculation and storage of the adjacency matrices of certain patterns. The exploitation of said graph in the extraction algorithms leads to a significant improvement in CPU and memory consumption. Experimental results on transactional databases of different natures confirm the effectiveness of the proposed approach.</p>