Name
Abstract | ||
|---|---|---|
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. (C) 2012 Elsevier B.V. All rights reserved. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1016/j.jag.2012.03.006 | International Journal of Applied Earth Observation and Geoinformation |
Keywords | Field | DocType |
Spatial statistics,Functional data analysis,Voronoi tessellation,Clustering,Bagging,Irradiance data | Spatial analysis,Functional data analysis,Data mining,Spatial dependence,Data set,Voronoi diagram,Environmental data,Dimensional reduction,Cluster analysis,Mathematics | Journal |
Volume | ISSN | Citations |
22 | 0303-2434 | 4 |
PageRank | References | Authors |
0.63 | 2 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Piercesare Secchi | 1 | 70 | 11.12 |
| Simone Vantini | 2 | 60 | 9.26 |
| Valeria Vitelli | 3 | 72 | 6.93 |