Clustering misaligned dependent curves applied to varved lake sediment for climate reconstruction

In this paper we introduce a novel functional clustering method, the Bagging Voronoi K-Medoid Aligment (BVKMA) algorithm, which simultaneously clusters and aligns spatially dependent curves. It is a nonparametric statistical method that does not rely on distributional or dependency structure assumpt...

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Bibliographic Details
Published in:Stochastic Environmental Research and Risk Assessment
Main Authors: Abramowicz, Konrad, Arnqvist, Per, SECCHI, PIERCESARE, Luna, Sara Sjöstedt de, VANTINI, SIMONE, VITELLI, VALERIA
Other Authors: Secchi, Piercesare, Vantini, Simone, Vitelli, Valeria
Format: Article in Journal/Newspaper
Language:English
Published: 2017
Subjects:
Clustering
Dependence
Functional data
Misalignment
Sediment data
Environmental Engineering
Environmental Chemistry
Water Science and Technology
Safety
Risk
Reliability and Quality
2300
Northern Sweden
Online Access:http://hdl.handle.net/11311/1022742
https://doi.org/10.1007/s00477-016-1287-6
http://link.springer-ny.com/link/service/journals/00477/index.htm
Description
Summary:In this paper we introduce a novel functional clustering method, the Bagging Voronoi K-Medoid Aligment (BVKMA) algorithm, which simultaneously clusters and aligns spatially dependent curves. It is a nonparametric statistical method that does not rely on distributional or dependency structure assumptions. The method is motivated by and applied to varved (annually laminated) sediment data from lake Kassjön in northern Sweden, aiming to infer on past environmental and climate changes. The resulting clusters and their time dynamics show great potential for seasonal climate interpretation, in particular for winter climate changes.

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