%0 Journal Article
%T Unsupervised Functional Data Clustering Based on Adaptive Weights
%A Yutong Gao
%A Shuang Chen
%J Open Journal of Statistics
%P 212-221
%@ 2161-7198
%D 2023
%I Scientific Research Publishing
%R 10.4236/ojs.2023.132011
%X 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.
%K Functional Data
%K Unsupervised Learning Clustering
%K Functional Principal Component Analysis
%K Adaptive Weight
%U http://www.scirp.org/journal/PaperInformation.aspx?PaperID=124497