统计与决策2026，Vol.42Issue(2)：31-38,8.DOI:10.13546/j.cnki.tjyjc.2026.02.005
基于广义Frechet距离的区间值函数型聚类方法
Interval-valued Functional Clustering Method Based on Generalized Frechet Distance
摘要
Abstract
Interval-valued functional clustering is a statistical analysis method that reveals the intrinsic structure of inter-val-valued functional data.The existing interval-valued functional clustering methods typically use absolute distances between function curves as similarity measures,neglecting the shape features and structural information of the function curves.These meth-ods are often influenced by data dimensionality and outliers,resulting in suboptimal clustering outcomes.In order to address the above deficiencies,this paper proposes a novel interval-valued functional clustering method.The method is based on the general-ized Frechet distance to measure the similarity between function curves and expresses distance information in interval form,which better captures the trend of function curve variations.Additionally,a tournament algorithm is introduced to enhance clustering effi-ciency.In the empirical studies,clustering analysis is conducted on temperature data from Chinese cities by using the proposed method,and the results are compared with clustering outcomes based on functional Manhattan distance and interval-valued func-tional Euclidean distance.The empirical results indicate that the proposed method outperforms other approaches in interval-val-ued functional clustering tasks.关键词
函数型数据/区间值函数型聚类/广义Frechet距离/聚类分析Key words
functional data/interval-valued functional clustering/generalized Frechet distance/clustering analysis分类
数理科学引用本文复制引用
何启志,曹腾腾,杜文豪..基于广义Frechet距离的区间值函数型聚类方法[J].统计与决策,2026,42(2):31-38,8.基金项目
江苏省社会科学基金资助项目(24GLB013) （24GLB013）
