为 K 媒介选择聚类数量的惩罚性标准

IF 1.4 2区数学 Q2 STATISTICS & PROBABILITY

Journal of Computational and Graphical Statistics Pub Date : 2024-02-29 DOI:10.1080/10618600.2024.2325458

Antoine Godichon-Baggioni, Sobihan Surendran

引用次数: 0

摘要

聚类是一种常用的无监督机器学习技术，可根据相似特征将数据点分组。我们在此重点讨论针对污染数据的无监督聚类，即...

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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A penalized criterion for selecting the number of clusters for K-medians

Clustering is a usual unsupervised machine learning technique for grouping the data points into groups based upon similar features. We focus here on unsupervised clustering for contaminated data, i...

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来源期刊

Journal of Computational and Graphical Statistics 数学-统计学与概率论

CiteScore

3.50

自引率

8.30%

发文量

153

审稿时长

>12 weeks

期刊介绍： The Journal of Computational and Graphical Statistics (JCGS) presents the very latest techniques on improving and extending the use of computational and graphical methods in statistics and data analysis. Established in 1992, this journal contains cutting-edge research, data, surveys, and more on numerical graphical displays and methods, and perception. Articles are written for readers who have a strong background in statistics but are not necessarily experts in computing. Published in March, June, September, and December.