Selection of the number of clusters in functional data analysis

Autor(es)

Zambom, Adriano Zanin ; Alfonso Collazos, Julian ; Dias, Ronaldo

• Tipo de documento: Artigo Científico
• Fonte: JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION; v. 92, n. 14, p. 19-pg., 2022-03-23.

Resumo

Identifying the number K of clusters in a dataset is one of the most difficult problems in clustering analysis. A choice of K that correctly characterizes the features of the data is essential for building meaningful clusters. In this paper we tackle the problem of estimating the number of clusters in functional data analysis by introducing a new measure that can be used with different procedures in selecting the optimal K. The main idea is to use a combination of two test statistics, which measure the lack of parallelism and the mean distance between curves, to compute criteria such as the within and between cluster sum of squares. Simulations in challenging scenarios suggest that procedures using this measure can detect the correct number of clusters more frequently than existing methods in the literature. The application of the proposed method is illustrated on several real datasets. (AU)

• Processo FAPESP: 19/04535-2 - Cadeias de Markov com alcance variável: incorporando variáveis exógenas
• Beneficiário: Nancy Lopes Garcia
• Modalidade de apoio: Auxílio à Pesquisa - Pesquisador Visitante - Internacional
• Processo FAPESP: 17/15306-9 - Incorporando Covariaveis Functionais em Modelos de Regressão Não Paramétricos
• Beneficiário: Nancy Lopes Garcia
• Modalidade de apoio: Auxílio à Pesquisa - Regular
• Processo FAPESP: 18/04654-9 - Séries temporais, ondaletas e dados de alta dimensão
• Beneficiário: Pedro Alberto Morettin
• Modalidade de apoio: Auxílio à Pesquisa - Temático