  Gradual pattern extraction is a field in (KDD) Knowledge Discovery in
Databases that maps correlations between attributes of a data set as gradual
dependencies. A gradual dependency may take a form of "the more Attribute K ,
the less Attribute L". In this paper, we propose an ant colony optimization
technique that uses a probabilistic approach to learn and extract frequent
gradual patterns. Through computational experiments on real-world data sets, we
compared the performance of our ant-based algorithm to an existing gradual item
set extraction algorithm and we found out that our algorithm outperforms the
later especially when dealing with large data sets.
