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

Ant Colony Optimization for Mining Gradual Patterns