Clustering with mixture models
Dong, Thi Kieu Trang (A.A. 2024/2025) Clustering with mixture models. Tesi di Laurea in Advanced statistics, Luiss Guido Carli, relatore Marta Catalano, pp. 70. [Master's Degree Thesis]
Full text for this thesis not available from the repository.
Abstract/Index
Clustering paradigms. K-means clustering (non-model-based). Model-based clustering: gaussian mixture models. EM and Gibbs sampling for GMMs. Frequentist estimation via the expectation-maximisation algorithm. Bayesian inference in finite gaussian mixture models via Gibbs sampling. Dirichlet process mixture models. Motivation. The dirichlet process. Stick-breaking construction. The Chinese restaurant process. Dirichlet process mixture models. Collapsed Gibbs sampling for DPMMs. Empirical results and comparison. Dataset description. K-means clustering. EM algorithm results. Bayesian GMM via Gibbs sampling. Dirichlet process mixture models: a practical attempt and lessons learned. Comparative analysis of clustering methods.
References
Bibliografia: pp. 64-65.
| Thesis Type: | Master's Degree Thesis |
|---|---|
| Institution: | Luiss Guido Carli |
| Degree Program: | Master's Degree Programs > Master's Degree Program in Data Science e Management (LM-91) |
| Chair: | Advanced statistics |
| Thesis Supervisor: | Catalano, Marta |
| Thesis Co-Supervisor: | Martino, Alessio |
| Academic Year: | 2024/2025 |
| Session: | Summer |
| Deposited by: | Alessandro Perfetti |
| Date Deposited: | 14 Jan 2026 11:49 |
| Last Modified: | 14 Jan 2026 11:49 |
| URI: | https://tesi.luiss.it/id/eprint/44657 |
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