The traditional algorithms that extract the gradual patterns often face the problem of managing the quantity of mined patterns, and in many applications, the calculation of all these patterns can prove to be intractable for the user-defined frequency threshold. Moreover, the concept of gradualness is defined just as an increase or a decrease variation. Indeed, a gradualness is considered as soon as the values of the attribute on both objects are different. This does not take into account the level of variation. Then, the variation of 10^-6 is considered as the same way as that of 10^6. As a result, numerous quantities of patterns extracted by traditional algorithms can be presented to the user, although their gradualness (due to the small variation) could be only a noise in the data. To address this issue, this paper suggests introducing the gradualness threshold from which to consider an increase or a decrease variation. In contrast to the literature approaches, the proposed approach takes into account the user's preferences on the gradualness threshold. The user knowledge could be used to fix the value of gradualness threshold. The proposed algorithm makes it possible to extract gradual patterns on certain databases where state-of-the-art gradual patterns mining algorithms fail due to too large search space. Moreover, results from an experimental evaluation on real databases show that the proposed algorithm is scalable, efficient, and can eliminate numerous patterns that do not verify specific gradualness requirements to show a small set of patterns to the user.