Selection of the Number of Clusters in Functional Data Analysis
Abstract
Identifying the number of clusters in a dataset is one of the most difficult problems in clustering analysis. A choice of 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 . 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.
Keywords
Cite
@article{arxiv.1905.00977,
title = {Selection of the Number of Clusters in Functional Data Analysis},
author = {Adriano Zanin Zambom and Julian A. Collazos and Ronaldo Dias},
journal= {arXiv preprint arXiv:1905.00977},
year = {2019}
}