聚类方法:优化还是不优化?
Michael Brusco1, Douglas Steinley2, Ashley L Watts3
1Department of Business Analytics, Information Systems and Supply Chain, College of Business, Florida State University.
Psychological methods
|September 12, 2024
概括
全球最佳集群解决方案可能并不总是与心理学理论或已知的数据结构保持一致. 虽然次优化解决方案有时可以在定义不佳的集群中提供边际收益,但优先考虑优化通常是建议的.
科学领域:
- 数据科学数据科学数据科学
- 心理统计 心理统计
- 机器学习 机器学习
背景情况:
- 聚类问题往往涉及优化一个客观标准.
- 全球最佳解决方案可能并不总是最易于解释或与真正的结构保持一致.
- 有时在模拟中观察到低于最佳的解决方案更好地与已知的集群结构保持一致.
研究的目的:
- 研究集群的全球最佳性与已知的集群结构的恢复之间的关系.
- 在控制全球最佳度偏差时检查K-中位数集群性能.
- 评估在集群中接受低于最佳解决方案的实际影响.
主要方法:
- 使用K-中位数集群进行模拟研究.
- 在集群解决方案中仔细控制全球最佳性偏差.
- 分析了优化集群标准和基础集群结构的恢复之间的对应关系.
主要成果:
- 非最佳的K-中位数集群解决方案偶尔会产生较少定义结构的实验数据的稍微更好的恢复.
- 在优化集群标准和已知的集群结构恢复之间没有始终观察到完美的对应.
- 该研究对全球最佳性偏差进行了控制,以评估其影响.
结论:
- 虽然在特定的,定义不佳的场景中,低于最佳的解决方案可能会带来轻微的优势,但接受它们通常是不明智的做法.
- 牺牲某种程度的优化是有原则的,当它满足理想的约束或改善其他相关标准时.
- 这些发现提醒人们不要误认为次优集群方法总是比优质集群方法更可取.
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