提出新方法对函数型数据聚类并剔除异常值。
funOCLUST: Clustering Functional Data with Outliers
- 基于OCLUST框架构建函数型聚类算法
- 在模拟与真实数据上均有效识别异常点
- 适合处理高维曲线数据的鲁棒分析
函数型数据因其无限维特性及对异常值的敏感性，给聚类带来独特挑战。本文提出OCLUST算法在函数设置下的扩展，借助该框架实现曲线聚类并剔除异常值。方法在模拟数据和真实函数数据集上进行评估，展现出良好的聚类性能与异常值识别能力。
原文摘要 · Abstract (English)
Functional data present unique challenges for clustering due to their infinite-dimensional nature and potential sensitivity to outliers. An extension of the OCLUST algorithm to the functional setting is proposed to address these issues. The approach leverages the OCLUST framework, creating a robust method to cluster curves and trim outliers. The methodology is evaluated on both simulated and real-world functional datasets, demonstrating strong performance in clustering and outlier identification.
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