J-score:对聚类准确性的一个可靠的衡量标准
Navid Ahmadinejad1,2, Yunro Chung1,2, Li Liu1,2
1Biodesign Institute, Arizona State University, Tempe, AZ, United States of America.
PeerJ. Computer science
|September 14, 2023
概括
我们开发了J-score,一种新的集群精度测量方法,以克服现有方法的局限性. 通过解决无匹配的集群和偏见,J-score有效地评估集群结果,提供了一个稳定和可解释的工具.
科学领域:
- 数据科学数据科学数据科学
- 机器学习 机器学习
- 统计 统计 统计 统计
背景情况:
- 聚类分析对于发现隐藏的数据结构至关重要.
- 对于集群的现有准确度指标存在一些问题,例如无法匹配的集群,偏差和糟糕的解释性.
- 强大的评估指标对于推进聚类算法和诊断模型至关重要.
研究的目的:
- 引入J-score,这是一个用于集群分析的新型准确度度.
- 解决当前集群精度测量的局限性,包括不匹配的集群和偏差.
- 为评估集群性能提供稳定,可解释和有效的指标.
主要方法:
- J-score量化了假设集群的真实类标签的恢复.
- 它采用双向集合匹配,使用Jaccard索引来建立类和集群之间的对应.
- 最终的J分数是按加权的雅卡德指数的和平均值计算出来的,反映了两个方向的调和.
主要成果:
- J-score有效地区分了与无匹配集群的分区结构.
- 它奖励对类数的准确推断,并减轻对过度集群的偏见.
- 该测量表明相对稳定的基线和简单的解释,通过模拟和真实世界的数据验证.
结论:
- J-score提供了一个有价值的,补充工具来评估聚类准确性.
- 它的设计解决了现有措施的关键缺陷,提高了可靠性和可解释性.
- 有一个R包,jScore,可用于实现J-score算法.
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