Gradual pattern extraction is a field in Knowledge Discovery in Databases that maps correlations between attributes of a data set as gradual dependencies. A gradual dependency may take the form: “the more Attribute\(_{K}\), the less Attribute\(_{L}\)”. Classical approa-ches for extracting gradual patterns extend either a breath-first search or a depth-first search strategy. However, these strategies can be computationally expensive and inefficient especially when dealing with large data sets. In this study, we investigate 3 population-based optimization techniques (i.e. ant colony optimization, genetic algorithm and particle swarm optimization) that may be employed improve the efficiency of mining gradual patterns. We show that ant colony optimization technique is better suited for gradual pattern mining task than the other 2 techniques. Through computational experiments on real-world data sets, we compared the computational performance of the proposed algorithms that implement the 3 population-based optimization techniques to classical algorithms for the task of gradual pattern mining and we show that the proposed algorithms outperform their classical counterparts.

渐进模式提取是数据库知识发现中的一个领域，它将数据集属性之间的相关性映射为渐进依赖关系。的逐渐依赖性可以采用以下形式：“所述多个属性\（_ {K} \） ，少属性\（_ {L} \）”。用于提取渐变模式的经典方法扩展了呼吸优先搜索或深度优先搜索策略。但是，这些策略在计算上可能很昂贵且效率低下，尤其是在处理大型数据集时。在这项研究中，我们研究了 3 种基于种群的优化技术（即蚁群优化、遗传算法和粒子群优化），可以用来提高挖掘渐变模式的效率。我们表明蚁群优化技术比其他两种技术更适合渐进模式挖掘任务。通过对真实世界数据集的计算实验，