| Full text | |
| Author(s): |
Zambom, Adriano Zanin
;
Alfonso Collazos, Julian
;
Dias, Ronaldo
Total Authors: 3
|
| Document type: | Journal article |
| Source: | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION; v. 92, n. 14, p. 19-pg., 2022-03-23. |
| Abstract | |
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) | |
| FAPESP's process: | 19/04535-2 - Covariate enabled variable length Markov Chains |
| Grantee: | Nancy Lopes Garcia |
| Support Opportunities: | Research Grants - Visiting Researcher Grant - International |
| FAPESP's process: | 17/15306-9 - Incorporating Functional covariates into nonparametric regression models |
| Grantee: | Nancy Lopes Garcia |
| Support Opportunities: | Regular Research Grants |
| FAPESP's process: | 18/04654-9 - Time series, wavelets and high dimensional data |
| Grantee: | Pedro Alberto Morettin |
| Support Opportunities: | Research Projects - Thematic Grants |