A robust method for cluster analysis

• 成果类型: Article
• 署名作者: Gallegos, MT; Ritter, G
• 署名单位: University of Passau
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/009053604000000940
• 发表日期: 2005
• 页码: 347-380
• 关键词: trimmed k-means; likelihood; criteria; algorithm; outliers

摘要

Let there be given a contaminated list of n R-d-valued observations coming from g different, normally distributed populations with a common covariance matrix. We compute the ML-estimator with respect to a certain statistical model with n - r outliers for the parameters of the g populations it detects outliers and simultaneously partitions their complement into g clusters. It turns out that the estimator unites both the minimum-covariance-determinant rejection method and the well-known pooled determinant criterion of cluster analysis. We also propose an efficient algorithm for approximating this estimator and study its breakdown points for mean values and pooled SSP matrix.