基于汉明距离的分类数据模型聚类

IF 3 1区数学 Q1 STATISTICS & PROBABILITY

Journal of the American Statistical Association Pub Date : 2024-09-20 DOI:10.1080/01621459.2024.2402568

Raffaele Argiento, Edoardo Filippi-Mazzola, Lucia Paci

引用次数: 0

摘要

本文提出了一种基于模型的方法，用于对无自然排序的分类数据进行聚类。所提出的方法利用汉明距离来定义概率质量函数族，从而对分类数据进行聚类。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Model-based clustering of categorical data based on the Hamming distance

A model-based approach is developed for clustering categorical data with no natural ordering. The proposed method exploits the Hamming distance to define a family of probability mass functions to m...

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来源期刊

Journal of the American Statistical Association 数学-统计学与概率论

CiteScore

7.50

自引率

8.10%

发文量

168

审稿时长

12 months

期刊介绍： Established in 1888 and published quarterly in March, June, September, and December, the Journal of the American Statistical Association ( JASA ) has long been considered the premier journal of statistical science. Articles focus on statistical applications, theory, and methods in economic, social, physical, engineering, and health sciences. Important books contributing to statistical advancement are reviewed in JASA . JASA is indexed in Current Index to Statistics and MathSci Online and reviewed in Mathematical Reviews. JASA is abstracted by Access Company and is indexed and abstracted in the SRM Database of Social Research Methodology.