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.

翻译：渐变模式提取是数据库(KDD)知识发现(KDD)的一个字段,它绘制了数据集属性与逐渐依赖性的相关性。渐进依赖性可能采取“更具属性的K, 较少属性的L”的形式。在本文件中,我们提议了一种蚂蚁聚群优化技术,使用概率法来学习和提取频繁的渐进模式。通过对现实世界数据集的计算实验,我们将我们的蚂蚁算法的性能与现有的渐进项目集提取算法作了比较,我们发现我们的算法比后来的好,特别是在处理大型数据集时。