4.5 Article

Ant colony optimization for mining gradual patterns

期刊

出版社

SPRINGER HEIDELBERG
DOI: 10.1007/s13042-021-01390-w

关键词

Ant colony optimization; Data mining; Genetic algorithm; Gradual patterns; Particle swarm optimization; Swarm intelligence

资金

  1. High Performance Computing Platform: MESO@LR - Occitanie / Pyrenees-Mediterranee Region
  2. Montpellier Mediterranean Metropole
  3. Montpellier University

向作者/读者索取更多资源

Gradual pattern extraction is a field in Knowledge Discovery in Databases that aims to map correlations between attributes of a data set as gradual dependencies. In this study, three population-based optimization techniques are investigated to improve the efficiency of mining gradual patterns. The results show that ant colony optimization technique outperforms genetic algorithm and particle swarm optimization in the task of gradual pattern mining.
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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据