动态频率子图挖掘算法在不断演变的图表:一个调查调查
Belgin Ergenç Bostanoğlu1, Nourhan Abuzayed1
1Computer Engineering, Izmir Institute of Technology, Izmir, Turkey.
PeerJ. Computer science
|December 9, 2024
概括
这篇评论比较了动态频率子图挖掘算法,用于演化的图形. 它强调了精确和近似方法的特点,确定了在这个具有挑战性的图形采矿领域的研究机会.
科学领域:
- 数据科学数据科学数据科学
- 图表采矿 图表采矿
- 机器学习 机器学习
背景情况:
- 频繁的子图挖掘 (FSM) 在数据科学中至关重要但具有挑战性.
- 现代应用程序使用不断演变的图形,增加FSM的复杂性.
- 现有的FSM算法与动态和大规模图形数据作斗争.
研究的目的:
- 为演变图形提供动态频率子图挖掘算法的比较审查.
- 分析和对比精确和近似的FSM算法,适用于动态图数据.
- 确定和讨论这个专业领域的未来研究方向.
主要方法:
- 基于诸如增量类型,图表表示和算法方法等属性的动态FSM算法的比较分析.
- 详细比较近似的动态FSM算法,包括采样策略和统计保证.
- 对适用于不断变化的图形结构的FSM技术进行系统审查.
主要成果:
- 基于关键特征的动态FSM算法的分类和比较.
- 对动态图的近似FSM方法的评估,重点关注其采样技术和目标.
- 确定动态子图采矿的研究缺口和机会.
结论:
- 这里介绍了动态FSM算法的全面概述,用于演变图形.
- 该评论作为动态图表挖掘研究人员的参考.
- 需要进一步的研究来应对FSM在不断变化的图形数据上的挑战.
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