S-Space College of Natural Sciences (자연과학대학) Dept. of Statistics (통계학과) Theses (Master's Degree_통계학과)
Finite mixture models and model-based clustering
- 자연과학대학 통계학과
- Issue Date
- 서울대학교 대학원
- EM algorithm; model selection; variable selection; diagnostics; two-dimensional gel electrophoresis data; magnitude magnetic resonance images.
- 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 임요한.
The Department of Statistics
The Graduate School
Seoul National University
Finite mixture models aims at identifying clusters of individuals who show
similar patterns. The method is having been used in a variety of fields,
especially in medicine to explain the idea of heterogeneity of treatment
effects on population. The number of mixture components is typically not
known and has to be chosen.
To solve this problem, EM algorithm-based approaches is
considered. We will review details of mixture models and model-based
clustering. Furthermore, we will provide an overview of several challenges
that have been only partially resolved.
Note: Writing this paper, I mainly refer to "Finite mixture models and model-based clustering"(Melnykov, 2010)
Keyword : EM algorithm, model selection, variable selection, diagnostics, two-dimensional gel electrophoresis data, magnitude magnetic resonance images.
Student Number : 2014-20298