集群有效性指数用于自动集群:全面审查
Abiodun M Ikotun1, Faustin Habyarimana1, Absalom E Ezugwu2
1School of Mathematics, Statistics, and Computer Science, University of KwaZulu-Natal, King Edward Avenue, Pietermaritzburg Campus, Pietermaritzburg, 3201, KwaZulu-Natal, South Africa.
本研究回顾了在元启发性聚类中使用的集群有效性指数 (CVI). 它识别和分析CVI,以帮助研究人员选择最佳的CVI,以获得最佳的算法性能.
科学领域:
- 数据科学数据科学数据科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 集群有效性指数 (CVI) 对于评估集群算法性能至关重要.
- 现有的CVI在它们的数学模型和适合不同集群任务的适用性方面有所不同.
- 超启发式算法依赖于CVI作为优化适应性函数.
研究的目的:
- 在基于元启发的自动集群算法中,作为健身函数使用的CVI进行系统审查.
- 分析各种CVI的特性和性能.
- 为研究人员在为特定应用选择合适的CVI时提供指导.
主要方法:
- 在元启发性聚类中对CVI进行系统的文献审查.
- 使用合成和现实世界数据集对常见CVI进行实验性评估.
- 用SOSK-means自动集群算法进行实验.
主要成果:
- 识别和分析了一系列在元启发式集群中使用的CVI.
- 在不同的数据集中展示了CVI的不同性能.
- 提供了关于共同CVI的优点和弱点的见解.
结论:
- 对CVI的全面理解对于有效的元启发性聚类至关重要.
- 选择CVI显著影响自动集群算法的性能.
- 本综述为在各种应用领域中选择最佳CVI提供了宝贵的资源.
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