Functional indexes for clustering in FDA
B. Pulido Bravo, A. M. Franco Pereira, R. E. Lillo Rodríguez
Clustering is considered as one of the most used techniques in Data Science. Clustering functional data is a challenging problem since it involves working in an infinite dimensional space. In this work this problem is addressed by applying the epigraph and the hypograph indexes to a functional dataset and thereby, converting it from a functional data problem into a multivariate problem. Once the multivariate dataset is obtained, the techniques that have been fully study in the literature for clustering multivariate data can be applied. The performance of this methodology is illustrated via a simulation study and considering different real datasets.
Keywords: Epigraph, Hypograph, Clustering, Functional data
Scheduled
GT06 Functional Data Analysis I. New methodologies
June 8, 2022 12:40 PM
Grade Hall
Other papers in the same session
F. Serra Burriel, P. Delicado, F. Cucchietti Tabanik, E. Graells-Garrido, A. Gil Julian, I. Eguskiza Martinez
H. A. Hernández Roig, P. Acedo, M. C. Aguilera-Morillo, R. E. Lillo, A. Menafoglio, A. Moreno-Oyervides, L. M. Sangalli
C. Yildirim, A. M. Franco Pereira, R. E. Lillo Rodriguez
A. Elías, J. M. Morales González, S. Pineda Morente
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6/8/22
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5/23/22
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