通过双重注意力融合和协作优化进行多视图图表集群.
Zuowei Wang1, Sen Xu2, Naixuan Guo3
1School of Information Engineering, Yancheng Institute of Technology, No.1 Hope Avenue Middle Road, Yancheng, 224051, Jiangsu, China.
概括
本研究介绍了通过双重注意力融合和协作优化 (MGCDC) 的多视图集群,这是一种有效的数据挖掘方法. MGCDC通过在多个数据视图中捕获独特和互补的信息来增强集群,以提高准确性.
科学领域:
- 数据挖掘 数据挖掘
- 机器学习 机器学习
- 图形理论 图形理论
背景情况:
- 多视图图表集群利用多个数据源进行节点分区.
- 现有的方法与独特的结构信息和交叉视图关系作斗争.
- 缺乏语义一致性导致不稳定的表示和糟糕的集群质量.
研究的目的:
- 提出一种新的端到端方法,通过双重注意力融合和协作优化 (MGCDC) 进行多视图图表集群,以改进多视图图表集群.
- 为了有效地捕捉每个视图中的独特结构信息和跨视图的互补关系.
- 强制执行全球语义一致性,以实现稳定的共识表示和增强集群质量.
主要方法:
- 使用图表注意力自编码器对视图特定节点嵌入进行每个视图的编码.
- 通过视图级别的注意力来整合嵌入,以实现统一的共识表示.
- 采用交叉视图集群对齐和语义一致性增强损失用于协作优化.
主要成果:
- 在五个基准数据集上,MGCDC取得了高度竞争力的表现.
- 该方法有效地整合了多视图信息,并完善了集群分配.
- 与最先进的方法相比,展示了优越的集群质量.
结论:
- MGCDC为多视图图表集群提供了一个强大的解决方案.
- 双重注意力融合和协作优化策略增强了集群性能.
- 提出的方法有效地解决了现有方法的局限性.
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