Addressing class imbalance in functional data clustering

Catherine Higgins () and Michelle Carey ()
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Catherine Higgins: University College Dublin, School of Mathematics and Statistics
Michelle Carey: University College Dublin, School of Mathematics and Statistics

Advances in Data Analysis and Classification, 2025, vol. 19, issue 4, No 7, 1023-1050

Abstract: Abstract The goal of functional clustering is twofold: first, to categorize curves with similar temporal behaviors into separate clusters, and second, to obtain a representative curve that summarizes the typical temporal behavior within each cluster. An important challenge in current functional clustering techniques is class imbalance, where some clusters contain a significantly greater number of curves than others. While class imbalance is extensively addressed in supervised classification, it remains relatively unexplored in unsupervised contexts. To address this gap, we propose adapting the iterative hierarchical clustering approach, originally designed for multivariate data, to the context of functional data. Thus introducing a novel method called functional iterative hierarchical clustering (funIHC) to effectively handle the clustering of imbalanced functional data. Through comprehensive simulation studies and benchmarking datasets, we demonstrate the effectiveness of the funIHC approach. Utilizing funIHC on gene expression data related to human influenza infection induced by the H3N2 virus, we identify five distinct and biologically meaningful patterns of gene expression. The R and MATLAB code for implementing funIHC is freely accessible at www.fdaatucd.com .

Keywords: Functional data analysis; Clustering; Class imbalance; Gene expression; 62H30; 62M10 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11634-024-00611-8

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