CLTD-LP:一种优化的自上而下的集群方法,具有线性前树,用于大数据集中的可扩展的频繁模式发现
M Sinthuja1, M Diviya2, P Saranya2
1School of Computer Science and Engineering, Vellore Institute of Technology, Vellore, India. sinthuja.m@vit.ac.in.
Scientific reports
|February 19, 2026
概括
本研究引入了一种新的自上而下的集群方法 (CLTDLP),用于高效的频繁项目集采矿. 与现有方法相比,CLTDLP算法显著减少了运行时间和内存使用量.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 算法优化的算法优化
背景情况:
- 频繁的项目集和关联规则提取在数据挖掘中至关重要.
- 当前线性前 (LP) 增长算法使用自下而上的方法,需要条件模式基础和LP树,这可能是低效的.
研究的目的:
- 提出一种新的线性前树,采用顶向下方法与集群 (CLTDLP) 方法.
- 解决现有的LP增长算法的局限性,特别是关于执行时间和内存利用的局限性.
主要方法:
- 该CLTD-LP算法采用了自上而下的方法.
- 它生成一个子标题表以有效地挖掘常见项目.
- 这种方法避免了创建条件模式基础或LP树.
主要成果:
- CLTD-LP算法在执行时间和内存利用方面表现出更高的效率.
- 在三个基准数据集中,CLTD-LP始终超过了现有的算法,如LP增长,OFIM和SSFIM.
- 观察到运行时间和内存的平均显著减少.
结论:
- 拟议的CLTD-LP算法为频繁的项目集采矿提供了更有利的方法.
- 与当前的方法相比,它在绩效指标上提供了实质性的改进.
- 对于高效的数据挖掘任务来说,CLTD-LP是一个有希望的进步.
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