论文标题
多目标关联规则挖掘的寒武纪爆炸算法
Cambrian Explosion Algorithm for Multi-Objective Association Rules Mining
论文作者
论文摘要
关联规则挖掘是研究最多的数据挖掘研究领域之一，其应用程序从杂货篮问题到高度解释的分类系统。经典关联规则挖掘算法有几个缺陷，尤其是关于其执行时间，记忆使用和产生的规则数。一种替代方法是使用荟萃术，这些元术已用于几个优化问题。本文有两个目标。首先，我们将对关联规则挖掘问题的最先进元数据术的表现进行比较。我们使用支持，信心和余弦使用这些算法的多目标版本。其次，我们提出了一种新的算法，旨在通过探索各种解决方案，类似于寒武纪爆炸的物种多样性，从而从大规模数据集中有效地挖掘规则。我们将我们的算法与22个现实世界数据集的20个基准算法进行比较，并表明我们的算法呈现出良好的结果，并且表现优于几个最先进的算法。
Association rule mining is one of the most studied research fields of data mining, with applications ranging from grocery basket problems to highly explainable classification systems. Classical association rule mining algorithms have several flaws especially with regards to their execution times, memory usage and number of rules produced. An alternative is the use of meta-heuristics, which have been used on several optimisation problems. This paper has two objectives. First, we provide a comparison of the performances of state-of-the-art meta-heuristics on the association rule mining problem. We use the multi-objective versions of those algorithms using support, confidence and cosine. Second, we propose a new algorithm designed to mine rules efficiently from massive datasets by exploring a large variety of solutions, akin to the explosion of species diversity of the Cambrian Explosion. We compare our algorithm to 20 benchmark algorithms on 22 real-world data-sets, and show that our algorithm present good results and outperform several state-of-the-art algorithms.
