Antoniadis, A., Brossat, X., Cugliari, J. and Poggi, J.M. (2013) Clustering Functional Data Using Wavelets. International Journal of Wavelets, Multiresolution and Information Processing, 11, 1350003.
https://doi.org/10.1142/S0219691313500033

has been cited by the following article:

• TITLE: Unsupervised Functional Data Clustering Based on Adaptive Weights

  AUTHORS: Yutong Gao, Shuang Chen

  KEYWORDS: Functional Data, Unsupervised Learning Clustering, Functional Principal Component Analysis, Adaptive Weight

  JOURNAL NAME: Open Journal of Statistics, Vol.13 No.2, April 26, 2023

  ABSTRACT: In recent years, functional data has been widely used in finance, medicine, biology and other fields. The current clustering analysis can solve the problems in finite-dimensional space, but it is difficult to be directly used for the clustering of functional data. In this paper, we propose a new unsupervised clustering algorithm based on adaptive weights. In the absence of initialization parameter, we use entropy-type penalty terms and fuzzy partition matrix to find the optimal number of clusters. At the same time, we introduce a measure based on adaptive weights to reflect the difference in information content between different clustering metrics. Simulation experiments show that the proposed algorithm has higher purity than some algorithms.