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Clustering compositional data trajectories: the case of particulate matter in the lower troposphere
Author(s) -
Bruno Francesca,
Cocchi Daniela,
Greco Fedele
Publication year - 2011
Publication title -
environmetrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.68
H-Index - 58
eISSN - 1099-095X
pISSN - 1180-4009
DOI - 10.1002/env.1128
Subject(s) - cluster analysis , smoothing , cluster (spacecraft) , computer science , compositional data , functional data analysis , data mining , focus (optics) , troposphere , metric (unit) , artificial intelligence , meteorology , physics , machine learning , engineering , operations management , optics , computer vision , programming language
Trajectories of compositional data, that is, sequences of composition measurements taken along a domain, can be considered as functional data. The present work centres on a way of clustering compositional data trajectories. Functional cluster analysis has been applied in several fields, but has not been extended to cope with the problem of clustering compositional data trajectories. In this work, we extend functional cluster analysis techniques to the analysis of compositional data using suitable compositional algebra both for smoothing observed trajectories and for building suitable metrics to evaluate dissimilarities between objects. When the focus is on the identification of typical shapes, clustering based on derivatives is the most suitable tool. As a motivating example, we consider clustering particulate matter vertical profiles in the lower troposphere. The impact of the choice of metric on the clustering structure is thoroughly discussed in this case study. Copyright © 2011 John Wiley & Sons, Ltd.