Trimmed k-means:: An attempt to robustify quantizers

• 成果类型: Article
• 署名作者: Cuesta-Albertos, JA; Gordaliza, A; Matrán, C
• 署名单位: Universidad de Cantabria; Universidad de Valladolid
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• 发表日期: 1997
• 页码: 553-576
• 关键词: DISTRIBUTIONS

摘要

A class of procedures based on impartial trimming (self-determined by the data) is introduced with the aim of robustifying k-means, hence the associated clustering analysis. We include a detailed study of optimal regions, showing that only nonpathological regions can arise from impartial trimming procedures. The asymptotic results provided in the paper focus on strong consistency of the suggested methods under widely general conditions. A section is devoted to exploring the performance of the procedure to detect anomalous data in simulated data sets.