数据流集群:为增量集群融合过程引入递归可扩展聚合函数
IEEE transactions on cybernetics
|March 3, 2025
概括
本研究引入了增量模糊数据流集群的新方法,使模型能够适应连续数据. 这种新的方法改善了集群比较和融合,优于现有的算法.
科学领域:
- 机器学习 机器学习
- 数据挖掘 数据挖掘
- 人工智能的人工智能
背景情况:
- 数据流 (DS) 学习需要因大量的连续数据而增加模型更新.
- 模糊的DS集群包括将数据吸收到现有的集群中或创建新的集群.
- 重叠的集群需要渐进的合并策略.
研究的目的:
- 为了正式化和操作化增量模糊集群比较用于数据流学习.
- 为增量融合过程开发强大的集群比较措施 (CMs).
- 为了提高模糊数据流集群算法的性能.
主要方法:
- 引入了用于增量融合的递归可扩展 (RE) 聚合函数.
- 提出了两个集群比较方法:基于RE函数的相似性和重叠性.
- 将增量CM集成到d-FuzzStream算法中进行分析.
主要成果:
- 证明了模糊星团的有效增量比较和融合.
- 拟议的RE聚合功能可以实现高效的在线处理.
- 增强的d-FuzzStream算法显示性能有所改善.
结论:
- 新的增量集群比较方法为模糊的DS集群提供了正式的基础.
- 新方法提高了数据流集群的适应性和准确性.
- 这项工作在现有的最先进的DS集群算法上取得了重大进展.
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