DOI: 10.1016/j.jag.2012.03.006
Scopus记录号: 2-s2.0-84883053801
论文题名: Bagging voronoi classifiers for clustering spatial functional data
作者: Secchi P ; , Vantini S ; , Vitelli V
刊名: International Journal of Applied Earth Observation and Geoinformation
ISSN: 15698432
出版年: 2013
卷: 22, 期: 1 起始页码: 53
结束页码: 64
语种: 英语
英文关键词: Bagging
; Clustering
; Functional data analysis
; Irradiance dat
; Spatial statistics
; Voronoi tessellation
Scopus关键词: algorithm
; cluster analysis
; data set
; spatial data
; statistical analysis
英文摘要: We propose a bagging strategy based on random Voronoi tessellations for the exploration of georeferenced functional data, suitable for different purposes (e.g., classification, regression, dimensional reduction, . . .). Urged by an application to environmental data contained in the Surface Solar Energy database, we focus in particular on the problem of clustering functional data indexed by the sites of a spatial finite lattice. We thus illustrate our strategy by implementing a specific algorithm whose rationale is to (i) replace the original data set with a reduced one, composed by local representatives of neighborhoods covering the entire investigated area; (ii) analyze the local representatives; (iii) repeat the previous analysis many times for different reduced data sets associated to randomly generated different sets of neighborhoods, thus obtaining many different weak formulations of the analysis; (iv) finally, bag together the weak analyses to obtain a conclusive strong analysis. Through an extensive simulation study, we show that this new procedure - which does not require an explicit model for spatial dependence - is statistically and computationally efficient. © 2012 Elsevier B.V.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/79835
Appears in Collections: 气候变化事实与影响
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作者单位: MOX - Dipartimento di Matematica, Politecnico di Milano, Piazza Leonardo da Vinci 32, 20133, Milano, Italy
Recommended Citation:
Secchi P,, Vantini S,, Vitelli V. Bagging voronoi classifiers for clustering spatial functional data[J]. International Journal of Applied Earth Observation and Geoinformation,2013-01-01,22(1)