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Clustering with varying risks of false assignments in discrete latent variable model

Title
Clustering with varying risks of false assignments in discrete latent variable model
Authors
Lee, DonghwanChoi, DongseokLee, Youngjo
Ewha Authors
이동환
SCOPUS Author ID
이동환scopus
Issue Date
2020
Journal Title
STATISTICAL METHODS IN MEDICAL RESEARCH
ISSN
0962-2802JCR Link

1477-0334JCR Link
Citation
STATISTICAL METHODS IN MEDICAL RESEARCH vol. 29, no. 10, pp. 2932 - 2944
Keywords
Clusteringfalse assignment rateextended likelihood
Publisher
SAGE PUBLICATIONS LTD
Indexed
SCIE; SCOPUS WOS scopus
Document Type
Article
Abstract
In clustering problems, to model the intrinsic structure of unlabeled data, the latent variable models are frequently used. These model-based clustering methods often provide a clustering rule minimizing the total false assignment error. However, in many clustering applications, it is desirable to treat false assignment errors for a certain cluster differently. In this paper, we introduce the false assignment rate for clustering and estimate it by using the extended likelihood approach. We propose VRclust, a novel clustering rule that controls various errors differently across clusters. Real data examples illustrate the usage of estimation of false assignment rate and a simulation study shows that error controls are consistent as the sample size increases.
DOI
10.1177/0962280220913067
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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