GENERALIZED DENSITY CLUSTERING

• 成果类型: Article
• 署名作者: Rinaldo, Alessandro; Wasserman, Larry
• 署名单位: Carnegie Mellon University
• 刊物名称: ANNALS OF STATISTICS
• ISSN/ISSBN: 0090-5364
• DOI: 10.1214/10-AOS797
• 发表日期: 2010
• 页码: 2678-2722
• 关键词: level sets; nonparametric-estimation; rates; CLASSIFICATION; CONVERGENCE; Consistency; CLASSIFIERS; estimators; STABILITY; support

摘要

We study generalized density-based clustering in which sharply defined clusters such as clusters on lower-dimensional manifolds are allowed. We show that accurate clustering is possible even in high dimensions. We propose two data-based methods for choosing the bandwidth and we study the stability properties of density clusters. We show that a simple graph-based algorithm successfully approximates the high density clusters.