一种基于多方网络的新聚类方法
1Center for the Study of Complexity, Babes-Bolyai University of Cluj-Napoca, Cluj Napoca, Cluj, Romania.
PeerJ. Computer science
|October 23, 2023
概括
本研究引入了一种新的基于网络的集群方法,使用多方网络. 该方法通过分析网络中的路径来有效地分组数据,显示与现有算法相比的竞争性性能.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 网络科学 网络科学
背景情况:
- 聚类是一个基本的机器学习任务,用于发现数据中隐藏的模式,没有先前的标签.
- 基于网络的集群算法很受欢迎,通常将数据表示为图形,节点作为数据实例,边缘作为相似度.
- 现有的方法在高维数据集中有效捕捉复杂关系方面面临挑战.
研究的目的:
- 提出一种新的基于网络的集群方法,利用多方网络结构.
- 通过网络路径分析来证明拟议方法在识别数据相似性的有效性.
- 评估新型聚类方法在合成和现实世界数据集 (包括健康和人口数据集) 上的性能.
主要方法:
- 拟议的方法构建了一个多方网络,其中层代表数据属性,节点代表数据间隔.
- 通过分析来自这个多方网络中的路径的信息来实现聚类.
- 该方法在合成数据集,现实世界的基准和世界银行的国家分组数据集上进行了测试.
主要成果:
- 新型多方网络集群方法的表现与最先进的方法相美.
- 该方法在特定数据集上表现出卓越的性能,突出了其在某些场景中的有效性.
- 应用到世界银行数据成功地根据健康,营养和人口指标对国家进行了分组.
结论:
- 拟议的多方网络方法为集群算法提供了一个有希望的新方向.
- 该方法提供了一种基于网络路径信息构建集群的直观方法.
- 该方法具有多功能性,适用于各种数据集和现实世界问题,并且与现有技术具有竞争力.
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