...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Intelligent data analysis >Multi-objective association rule mining with binary bat algorithm
【24h】

Multi-objective association rule mining with binary bat algorithm

机译：二进制蝙蝠算法的多目标关联规则挖掘

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Association rule mining meeting a variety of measures is regarded as a multi-objective optimization problem rather than a single objective optimization problem. The convergent speed of traditional multi-objective algorithms such as genetic algorithm is slow and the efficiency of these algorithms is low. Furthermore, the rules generated by traditional multi-objective algorithms are too large to be efficiently analyzed and explored in any further process. Bat algorithm is a new efficient global optimal algorithm whose convergence is superior to binary particle swarm optimization (BPSO) and genetic algorithm. This paper discusses the application of multi-objective bat algorithm to association rule mining. We propose multi-objective binary bat algorithm (MBBA) based on Pareto for association rule mining. This algorithm is independent of minimum support and minimum confidence. To evaluate the association rules mined by MBBA algorithm, we propose a new method to discover interesting association rules without favoring or excluding any measure. Compared with the single-objective BPSO, binary bat algorithm (BBA) and Apriori algorithm, the experimental results on six datasets show that the new algorithm is feasible and highly effective. It can make up the shortage of single objective algorithms and traditional association rule mining algorithms.
机译：满足各种度量标准的关联规则挖掘被视为多目标优化问题，而不是单目标优化问题。传统的多目标算法，例如遗传算法，收敛速度慢，效率低。此外，传统的多目标算法生成的规则太大，无法在任何后续过程中进行有效的分析和探索。蝙蝠算法是一种新型的高效全局最优算法，其收敛性优于二进制粒子群算法（BPSO）和遗传算法。本文讨论了多目标蝙蝠算法在关联规则挖掘中的应用。我们提出了基于Pareto的多目标二进制蝙蝠算法（MBBA）用于关联规则挖掘。该算法独立于最小支持和最小置信度。为了评估通过MBBA算法挖掘的关联规则，我们提出了一种新的方法来发现有趣的关联规则，而不偏爱或排除任何措施。与单目标BPSO，二进制蝙蝠算法（BBA）和Apriori算法相比，在六个数据集上的实验结果表明，该算法是可行且高效的。它可以弥补单一目标算法和传统关联规则挖掘算法的不足。

著录项

  • 来源
    《Intelligent data analysis》 |2016年第1期|105-128|共24页
  • 作者

    Song Anping; Ding Xuehai; Chen Jianjiao; Li Mingbo; Cao Wei; Pu Ke;

  • 作者单位

    Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China;

    Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China;

    Yale Univ, Sch Med, Yale Stem Cell Ctr, New Haven, CT USA|Yale Univ, Sch Med, Dept Cell Biol, New Haven, CT 06510 USA;

    Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China;

    Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China;

    Shanghai Univ, Sch Comp Engn & Sci, Shanghai 200444, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Association rule mining; bat algorithm; multi-objective; Pareto fronter;

    机译：关联规则挖掘;蝙蝠算法;多目标;帕累托前沿;

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号