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¡¶ÄϾ©Ê¦´óѧ±¨£¨×ÔÈ»¿Æѧ°æ£©¡·[ISSN:1001-4616/CN:32-1239/N]

Issue:

2022Äê04ÆÚ

Page:

1-9

Research Field:

Êýѧ

Publishing date:

Info

Title:

A New Functional Data Clustering Method Based on Directional Multiple Hypothesis Test and Information Entropy

Author(s):

Du Xiuli; Jiang Xiaohu; Sun Chentong; Yu Zheng

(School of Mathematical Sciences,Nanjing Normal University,Nanjing 210023,China)

Keywords:

functional data clustering analysis; false discovery rate; directional multiple hypothesis testing; the information entropy; parallelism

PACS:

O212.1

DOI:

10.3969/j.issn.1001-4616.2022.04.001

Abstract:

In recent years,clustering analysis for functional data has been developed to a certain extent. However,when the data belong to infinite dimensional space,it will bring some difficulty to clustering. The limitations of traditional clustering methods are increasingly prominent in the clustering process of functional data. Therefore,this paper proposes a new clustering method for functional data,which can better adapt to the characteristics of data and achieve better clustering effect. Firstly,based on the directional multiple hypothesis test of false discover rate and the theoretical basis of information entropy,a new parallelism statistic is proposed to describe the morphological differences of functional curves. On this basis,a new calculation formula of proximity is proposed,and finally the condensed hierarchical clustering algorithm is improved. The new clustering method is applied to four different types of functional data sets,and the clustering results are analyzed and compared with other existing methods,which proves the effectiveness and advantages of the improved condensed hierarchical clustering method.

References:

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Memo

Memo:

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Common functions

Last Update: 2022-12-15