Model based clustering of functional data with mild outliers
Author
Faculty Advisor
Date
2023
Keywords
functional data, model-based clustering, contaminated normal distribution, EM algorithm
Abstract (summary)
We propose a procedure, called CFunHDDC, for clustering functional data with mild outliers which combines two existing clustering methods: the functional high dimensional data clustering (FunHDDC) [1] and the contaminated normal mixture (CNmixt) [3] method for multivariate data. We adapt the FunHDDC approach to data with mild outliers by considering a mixture of multivariate contaminated normal distributions. To fit the functional data in group-specific functional subspaces we extend the parsimonious models considered in FunHDDC, and we estimate the model parameters using an expectation-conditional maximization algorithm (ECM). The performance of the proposed method is illustrated for simulated and real-world functional data, and CFunHDDC outperforms FunHDDC when applied to functional data with outliers.
Publication Information
Anton, C., & Smith, I. (2023). Model based clustering of functional data with mild outliers. In P. Brito, J.G. Dias, B. Lausen, A. Montanari, & R. Nugent (Eds), Classification and data science in the digital age. IFCS 2022. Studies in classification, data analysis, and knowledge organization. Springer, Cham. https://doi.org/10.1007/978-3-031-09034-9_2
Notes
Item Type
Book Chapter
Language
Rights
Attribution (CC BY)
