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<p>Clustering long term follow-up data has broad applications in supervised and unsupervised learning. This study proposes to accumulate the dissimilarity measure across the study interval to provide an overall index for clustering. The data, typically non-Gaussian, are assumed to be collected at regular time points, and dissimilarity is calculated as a rank-based noncentrality. To initiate the clustering, the K-means and a discretized K-center method of functional principal component analysis is used. We propose a fast computational algorithm to speed up the clustering process with tolerable sacrifice in correct rates. The proposed algorithm is illustrated through simulation studies. For real-world data analysis, a year of fine particulate matter (PM<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(_{2.5}\)</EquationSource> </InlineEquation>) monitoring data are analyzed for exploring the spatial closeness between and within clusters.</p>

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Clustering for spatial monitoring data for air-pollution with regular sample points

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月2日

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  • Hong-Dar Isaac Wu

摘要

Clustering long term follow-up data has broad applications in supervised and unsupervised learning. This study proposes to accumulate the dissimilarity measure across the study interval to provide an overall index for clustering. The data, typically non-Gaussian, are assumed to be collected at regular time points, and dissimilarity is calculated as a rank-based noncentrality. To initiate the clustering, the K-means and a discretized K-center method of functional principal component analysis is used. We propose a fast computational algorithm to speed up the clustering process with tolerable sacrifice in correct rates. The proposed algorithm is illustrated through simulation studies. For real-world data analysis, a year of fine particulate matter (PM \(_{2.5}\) ) monitoring data are analyzed for exploring the spatial closeness between and within clusters.