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Finite mixture models and model-based clustering
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 임요한 | - |
| dc.contributor.author | 김경민 | - |
| dc.date.accessioned | 2017-07-19T08:47:26Z | - |
| dc.date.available | 2017-07-19T08:47:26Z | - |
| dc.date.issued | 2017-02 | - |
| dc.identifier.other | 000000140654 | - |
| dc.identifier.uri | https://hdl.handle.net/10371/131325 | - |
| dc.description | 학위논문 (석사)-- 서울대학교 대학원 : 통계학과, 2017. 2. 임요한. | - |
| dc.description.abstract | Abstract
Kyoung-Min Kim 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 | - |
| dc.description.tableofcontents | 1. Introduction 1
2. Inference in finite mixture models 2 2.1 Estimation in finite mixture models 2 2.2 Challenges in implementation 3 2.2.1 Unbounded likelihood functions 3 2.2.2 Initialization of the EM Algorithm 3 2.3 Model selection 4 2.3.1 Choosing the optimal number of components 4 2.3.2 Variable selection 5 3. Some recent applications 6 3.1 Magnitude magnetic resonance imaging data 6 3.2 Finite mixtures models in surveys 6 4. Some additional topics and challenges 8 4.1. Hierarchical model-based clustering and cluster merging 8 4.2 Non-parametric approaches to mixture modeling and model-based clustering 8 4.3 Semi-supervised clustering 9 4.4 Constrained clustering 9 4.5 Diagnostics 10 4.6 Robust and skewed mixture models 10 4.7 Dependent data 11 5. Conclusion 12 References 13 | - |
| dc.format | application/pdf | - |
| dc.format.extent | 353551 bytes | - |
| dc.format.medium | application/pdf | - |
| dc.language.iso | ko | - |
| dc.publisher | 서울대학교 대학원 | - |
| dc.subject | EM algorithm | - |
| dc.subject | model selection | - |
| dc.subject | variable selection | - |
| dc.subject | diagnostics | - |
| dc.subject | two-dimensional gel electrophoresis data | - |
| dc.subject | magnitude magnetic resonance images. | - |
| dc.subject.ddc | 519 | - |
| dc.title | Finite mixture models and model-based clustering | - |
| dc.type | Thesis | - |
| dc.description.degree | Master | - |
| dc.citation.pages | 17 | - |
| dc.contributor.affiliation | 자연과학대학 통계학과 | - |
| dc.date.awarded | 2017-02 | - |
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