In recent years, research in Knowledge Discovery in Databases has been a topic of increasing interest for many researchers. Currently, a variety of such research has effectively been used in many areas of life. Interestingness measures play an important role in the field of research in knowledge discovery. Therefore, the study of the classification of interestingness measures is also an important topic for researchers. Classification of measures is mostly based on two main methods: classification based on the properties of measures and classification based on the behavior of measures. In this study, we propose a new classification method focusing on the research and study of the value variation of the objective interestingness measures that satisfy the asymmetric nature, by taking the partial derivative of the function that calculates the value of interestingness measures according to the 2×2 contingency table. Our results show that asymmetrical objective interestingness measures are classified by considering the increasing, decreasing or stable derivative formula for each measures.