一种基于相对密度的无参数双聚类方法,用于识别非线性特征关系
Namita Jain1, Susmita Ghosh1, Ashish Ghosh2
1Department of Computer Science and Engineering, Jadavpur University, Kolkata 700032, India.
Heliyon
|August 19, 2024
概括
新的PF-RelDenBi方法使用局部密度变化来识别双集群,克服了现有的算法对非线性和非单调特征关系的局限性,而不需要用户参数. 它在检测跨各种数据集的双集群方面表现出卓越的性能.
科学领域:
- 数据挖掘和机器学习
- 生物信息学和计算生物学
背景情况:
- 传统的双聚类算法通常依赖于诸如线性或单调性之类的限制性假设.
- 由于全球密度标准,现有的基于密度的方法可能会错过双集群.
研究的目的:
- 引入PF-RelDenBi,这是一种新的双算法,可以根据本地特征密度变化识别双.
- 通过处理非线性和非单调的特征关系来克服现有方法的局限性.
- 开发一个无参数的算法,适用于各种数据集.
主要方法:
- 对于特征对,PF-RelDenBi利用边缘密度和联合密度的局部变化来识别观测子集.
- 使用非线性特征关系索引,找到由共同观测连接的特征集,形成双集群.
- 该算法在不需要用户定义参数的情况下运行.
主要成果:
- 与11个最先进的算法相比,PF-RelDenBi在大多数模拟数据集上表现出卓越的性能.
- 在基准数据集上检测到的双集群在作为附加功能使用时改善了分类性能.
- 在三个基准数据集上,PF-RelDenBi比11种比较方法取得了更高的准确性,NMI和ARI.
- 对COVID-19数据集的应用确定了影响疾病传播的人口特征.
结论:
- PF-RelDenBi有效地识别了具有非线性和非单调特征关系的双集群.
- 无参数性质和强大的性能使其适用于各种数据挖掘应用.
- 该方法在特征工程方面显示出有前途,用于改进分类和识别影响疾病传播的因素.
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