Gradual Patterns allow to express frequent patterns like "the older an employee, the higher his/her income". After having been extensively used for fuzzy command (e.g., "the closer the wall, the harder the break"), they are currently grasping the attention of the data mining community, where the focus is put on the scalability of the methods when addressing very large databases (e.g. biology). However, it is often the case that the information is not contained at the upper and lower boundaries (min/max), but rather hidden in-between. For instance, the most interesting rule could be "the closer the age of an employee to 46, the higher his/her income". In this paper, we introduce several methods for automatically defining fuzzy modalities and we compare the results obtained when considering the various solutions.