Clustering with varying risks of false assignments in discrete latent variable model
- 주제(키워드) Clustering , extended likelihood , false assignment rate
- 등재 SCIE, SCOPUS
- 발행기관 SAGE Publications Ltd
- 발행년도 2020
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000172258
- 본문언어 영어
- Published As https://dx.doi.org/10.1177/0962280220913067
- PubMed https://pubmed.ncbi.nlm.nih.gov/32216581
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
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. © The Author(s) 2020.
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