funOCLUST: Clustering Functional Data with Outliers
This work addresses clustering challenges in functional data analysis, which is incremental as it adapts an existing algorithm to a specific domain.
The authors tackled the problem of clustering functional data with outliers by extending the OCLUST algorithm to handle curves, resulting in a robust method that demonstrated strong performance in clustering and outlier identification on simulated and real-world datasets.
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.