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You searched for subject:(Model based clustering). Showing records 1 – 30 of 53 total matches.

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McMaster University

1. Pesevski, Angelina. Subspace Clustering with the Multivariate-t Distribution.

Degree: MSc, 2017, McMaster University

Clustering procedures suitable for the analysis of very high-dimensional data are needed for many modern data sets. One model-based clustering approach called high-dimensional data clustering(more)

Subjects/Keywords: Model-Based Clustering; High-Dimensional

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APA (6th Edition):

Pesevski, A. (2017). Subspace Clustering with the Multivariate-t Distribution. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/22311

Chicago Manual of Style (16th Edition):

Pesevski, Angelina. “Subspace Clustering with the Multivariate-t Distribution.” 2017. Masters Thesis, McMaster University. Accessed November 22, 2019. http://hdl.handle.net/11375/22311.

MLA Handbook (7th Edition):

Pesevski, Angelina. “Subspace Clustering with the Multivariate-t Distribution.” 2017. Web. 22 Nov 2019.

Vancouver:

Pesevski A. Subspace Clustering with the Multivariate-t Distribution. [Internet] [Masters thesis]. McMaster University; 2017. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/11375/22311.

Council of Science Editors:

Pesevski A. Subspace Clustering with the Multivariate-t Distribution. [Masters Thesis]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/22311


McMaster University

2. Zhang, Xu Xuan. Non-Gaussian Mixture Model Averaging for Clustering.

Degree: MSc, 2016, McMaster University

The Gaussian mixture model has been used for model-based clustering analysis for decades. Most model-based clustering analyses are based on the Gaussian mixture model. Model(more)

Subjects/Keywords: Model-based Clustering; Model Averaging; Mixture Models

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APA (6th Edition):

Zhang, X. X. (2016). Non-Gaussian Mixture Model Averaging for Clustering. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/20792

Chicago Manual of Style (16th Edition):

Zhang, Xu Xuan. “Non-Gaussian Mixture Model Averaging for Clustering.” 2016. Masters Thesis, McMaster University. Accessed November 22, 2019. http://hdl.handle.net/11375/20792.

MLA Handbook (7th Edition):

Zhang, Xu Xuan. “Non-Gaussian Mixture Model Averaging for Clustering.” 2016. Web. 22 Nov 2019.

Vancouver:

Zhang XX. Non-Gaussian Mixture Model Averaging for Clustering. [Internet] [Masters thesis]. McMaster University; 2016. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/11375/20792.

Council of Science Editors:

Zhang XX. Non-Gaussian Mixture Model Averaging for Clustering. [Masters Thesis]. McMaster University; 2016. Available from: http://hdl.handle.net/11375/20792


University of Guelph

3. O'Reilly, Rachel. Cross-Validation for Model Selection in Model-Based Clustering .

Degree: 2012, University of Guelph

Clustering is a technique used to partition unlabelled data into meaningful groups. This thesis will focus on the area of clustering called model-based clustering, where… (more)

Subjects/Keywords: model-based clustering; model selection; cross-validation

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APA (6th Edition):

O'Reilly, R. (2012). Cross-Validation for Model Selection in Model-Based Clustering . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3911

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

O'Reilly, Rachel. “Cross-Validation for Model Selection in Model-Based Clustering .” 2012. Thesis, University of Guelph. Accessed November 22, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3911.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

O'Reilly, Rachel. “Cross-Validation for Model Selection in Model-Based Clustering .” 2012. Web. 22 Nov 2019.

Vancouver:

O'Reilly R. Cross-Validation for Model Selection in Model-Based Clustering . [Internet] [Thesis]. University of Guelph; 2012. [cited 2019 Nov 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3911.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

O'Reilly R. Cross-Validation for Model Selection in Model-Based Clustering . [Thesis]. University of Guelph; 2012. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3911

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Alberta

4. Moulavi, Davoud. Finding, Evaluating and Exploring Clustering Alternatives Unsupervised and Semi-supervised.

Degree: PhD, Department of Computing Science, 2014, University of Alberta

Clustering aims at grouping data objects into meaningful clusters using no (or only a small amount of) supervision. This thesis studies two major clustering paradigms:… (more)

Subjects/Keywords: Hierarchical Density-Based Clustering; Density-Based Clustering Validation; Density-Based Clustering; Semi-supervised Clustering; Semi-supervised Model Selection

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APA (6th Edition):

Moulavi, D. (2014). Finding, Evaluating and Exploring Clustering Alternatives Unsupervised and Semi-supervised. (Doctoral Dissertation). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/xp68kg944

Chicago Manual of Style (16th Edition):

Moulavi, Davoud. “Finding, Evaluating and Exploring Clustering Alternatives Unsupervised and Semi-supervised.” 2014. Doctoral Dissertation, University of Alberta. Accessed November 22, 2019. https://era.library.ualberta.ca/files/xp68kg944.

MLA Handbook (7th Edition):

Moulavi, Davoud. “Finding, Evaluating and Exploring Clustering Alternatives Unsupervised and Semi-supervised.” 2014. Web. 22 Nov 2019.

Vancouver:

Moulavi D. Finding, Evaluating and Exploring Clustering Alternatives Unsupervised and Semi-supervised. [Internet] [Doctoral dissertation]. University of Alberta; 2014. [cited 2019 Nov 22]. Available from: https://era.library.ualberta.ca/files/xp68kg944.

Council of Science Editors:

Moulavi D. Finding, Evaluating and Exploring Clustering Alternatives Unsupervised and Semi-supervised. [Doctoral Dissertation]. University of Alberta; 2014. Available from: https://era.library.ualberta.ca/files/xp68kg944


McMaster University

5. Pathmanathan, Thinesh. Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.

Degree: MSc, 2018, McMaster University

Model-based clustering is a probabilistic approach that views each cluster as a component in an appropriate mixture model. The Gaussian mixture model is one of… (more)

Subjects/Keywords: Model-based clustering; dimension reduction; statistical learning

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Pathmanathan, T. (2018). Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/22758

Chicago Manual of Style (16th Edition):

Pathmanathan, Thinesh. “Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.” 2018. Masters Thesis, McMaster University. Accessed November 22, 2019. http://hdl.handle.net/11375/22758.

MLA Handbook (7th Edition):

Pathmanathan, Thinesh. “Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions.” 2018. Web. 22 Nov 2019.

Vancouver:

Pathmanathan T. Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. [Internet] [Masters thesis]. McMaster University; 2018. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/11375/22758.

Council of Science Editors:

Pathmanathan T. Dimension Reduction and Clustering of High Dimensional Data using a Mixture of Generalized Hyperbolic Distributions. [Masters Thesis]. McMaster University; 2018. Available from: http://hdl.handle.net/11375/22758


University of Rochester

6. Ma, Fei; Hyrien, Ollivier. Composite Likelihood Inference for Multivariate Finite Mixture Models and Application to Flow Cytometry.

Degree: PhD, 2014, University of Rochester

 Finite mixture models nd numerous applications in various elds, including in, but not limited to, the analysis of flow cytometry data. These models offer flexible… (more)

Subjects/Keywords: Model-Based Clustering; Copula; Semi-Parametric; Model Selection

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APA (6th Edition):

Ma, Fei; Hyrien, O. (2014). Composite Likelihood Inference for Multivariate Finite Mixture Models and Application to Flow Cytometry. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28956

Chicago Manual of Style (16th Edition):

Ma, Fei; Hyrien, Ollivier. “Composite Likelihood Inference for Multivariate Finite Mixture Models and Application to Flow Cytometry.” 2014. Doctoral Dissertation, University of Rochester. Accessed November 22, 2019. http://hdl.handle.net/1802/28956.

MLA Handbook (7th Edition):

Ma, Fei; Hyrien, Ollivier. “Composite Likelihood Inference for Multivariate Finite Mixture Models and Application to Flow Cytometry.” 2014. Web. 22 Nov 2019.

Vancouver:

Ma, Fei; Hyrien O. Composite Likelihood Inference for Multivariate Finite Mixture Models and Application to Flow Cytometry. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/1802/28956.

Council of Science Editors:

Ma, Fei; Hyrien O. Composite Likelihood Inference for Multivariate Finite Mixture Models and Application to Flow Cytometry. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28956

7. Li, Xuan. Statistical Inference for High-Dimensional Genetic Data.

Degree: PhD, Mathematics & Statistics, 2019, York University

 This dissertation focuses on three types of high-dimensional genetic data: protein sequences, DNA methylation data, and microRNA expression data. The four major parts are presented… (more)

Subjects/Keywords: Statistics; Statistical genetics; High-dimensional data; Clustering categorical data; Model-based clustering; Two-sample problem

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APA (6th Edition):

Li, X. (2019). Statistical Inference for High-Dimensional Genetic Data. (Doctoral Dissertation). York University. Retrieved from http://hdl.handle.net/10315/35894

Chicago Manual of Style (16th Edition):

Li, Xuan. “Statistical Inference for High-Dimensional Genetic Data.” 2019. Doctoral Dissertation, York University. Accessed November 22, 2019. http://hdl.handle.net/10315/35894.

MLA Handbook (7th Edition):

Li, Xuan. “Statistical Inference for High-Dimensional Genetic Data.” 2019. Web. 22 Nov 2019.

Vancouver:

Li X. Statistical Inference for High-Dimensional Genetic Data. [Internet] [Doctoral dissertation]. York University; 2019. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/10315/35894.

Council of Science Editors:

Li X. Statistical Inference for High-Dimensional Genetic Data. [Doctoral Dissertation]. York University; 2019. Available from: http://hdl.handle.net/10315/35894


McMaster University

8. Patel, Nidhi. Longitudinal Clustering via Mixtures of Multivariate Power Exponential Distributions.

Degree: MSc, 2016, McMaster University

A mixture model approach for clustering longitudinal data is introduced. The approach, which is based on mixtures of multivariate power exponential distributions, allows for varying… (more)

Subjects/Keywords: longitudinal data; model-based clustering; mixture models; power exponential distribution

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APA (6th Edition):

Patel, N. (2016). Longitudinal Clustering via Mixtures of Multivariate Power Exponential Distributions. (Masters Thesis). McMaster University. Retrieved from http://hdl.handle.net/11375/20406

Chicago Manual of Style (16th Edition):

Patel, Nidhi. “Longitudinal Clustering via Mixtures of Multivariate Power Exponential Distributions.” 2016. Masters Thesis, McMaster University. Accessed November 22, 2019. http://hdl.handle.net/11375/20406.

MLA Handbook (7th Edition):

Patel, Nidhi. “Longitudinal Clustering via Mixtures of Multivariate Power Exponential Distributions.” 2016. Web. 22 Nov 2019.

Vancouver:

Patel N. Longitudinal Clustering via Mixtures of Multivariate Power Exponential Distributions. [Internet] [Masters thesis]. McMaster University; 2016. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/11375/20406.

Council of Science Editors:

Patel N. Longitudinal Clustering via Mixtures of Multivariate Power Exponential Distributions. [Masters Thesis]. McMaster University; 2016. Available from: http://hdl.handle.net/11375/20406


McMaster University

9. Wong, Monica. Topics in One-Way Supervised Biclustering Using Gaussian Mixture Models.

Degree: PhD, 2017, McMaster University

Cluster analysis identifies homogeneous groups that are relevant within a population. In model-based clustering, group membership is estimated using a parametric finite mixture model, commonly… (more)

Subjects/Keywords: Biclustering; One-way supervision; Finite mixture models; Model-based clustering

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APA (6th Edition):

Wong, M. (2017). Topics in One-Way Supervised Biclustering Using Gaussian Mixture Models. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/21065

Chicago Manual of Style (16th Edition):

Wong, Monica. “Topics in One-Way Supervised Biclustering Using Gaussian Mixture Models.” 2017. Doctoral Dissertation, McMaster University. Accessed November 22, 2019. http://hdl.handle.net/11375/21065.

MLA Handbook (7th Edition):

Wong, Monica. “Topics in One-Way Supervised Biclustering Using Gaussian Mixture Models.” 2017. Web. 22 Nov 2019.

Vancouver:

Wong M. Topics in One-Way Supervised Biclustering Using Gaussian Mixture Models. [Internet] [Doctoral dissertation]. McMaster University; 2017. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/11375/21065.

Council of Science Editors:

Wong M. Topics in One-Way Supervised Biclustering Using Gaussian Mixture Models. [Doctoral Dissertation]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/21065


Brigham Young University

10. Davis, Aaron Samuel. Bisecting Document Clustering Using Model-Based Methods.

Degree: MS, 2009, Brigham Young University

 We all have access to large collections of digital text documents, which are useful only if we can make sense of them all and distill… (more)

Subjects/Keywords: document clustering; text mining; model-based; Computer Sciences

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APA (6th Edition):

Davis, A. S. (2009). Bisecting Document Clustering Using Model-Based Methods. (Masters Thesis). Brigham Young University. Retrieved from https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2937&context=etd

Chicago Manual of Style (16th Edition):

Davis, Aaron Samuel. “Bisecting Document Clustering Using Model-Based Methods.” 2009. Masters Thesis, Brigham Young University. Accessed November 22, 2019. https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2937&context=etd.

MLA Handbook (7th Edition):

Davis, Aaron Samuel. “Bisecting Document Clustering Using Model-Based Methods.” 2009. Web. 22 Nov 2019.

Vancouver:

Davis AS. Bisecting Document Clustering Using Model-Based Methods. [Internet] [Masters thesis]. Brigham Young University; 2009. [cited 2019 Nov 22]. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2937&context=etd.

Council of Science Editors:

Davis AS. Bisecting Document Clustering Using Model-Based Methods. [Masters Thesis]. Brigham Young University; 2009. Available from: https://scholarsarchive.byu.edu/cgi/viewcontent.cgi?article=2937&context=etd


University of Guelph

11. Franczak, Brian. Mixtures of Shifted Asymmetric Laplace Distributions .

Degree: 2014, University of Guelph

 In this thesis we introduce a mixture of shifted asymmetric Laplace (SAL) distributions for model-based clustering and classification. The mixture of SAL distributions allows for… (more)

Subjects/Keywords: finite mixture models; model-based clustering; multivariate analysis; asymmetric Laplace

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APA (6th Edition):

Franczak, B. (2014). Mixtures of Shifted Asymmetric Laplace Distributions . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8006

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Franczak, Brian. “Mixtures of Shifted Asymmetric Laplace Distributions .” 2014. Thesis, University of Guelph. Accessed November 22, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8006.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Franczak, Brian. “Mixtures of Shifted Asymmetric Laplace Distributions .” 2014. Web. 22 Nov 2019.

Vancouver:

Franczak B. Mixtures of Shifted Asymmetric Laplace Distributions . [Internet] [Thesis]. University of Guelph; 2014. [cited 2019 Nov 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8006.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Franczak B. Mixtures of Shifted Asymmetric Laplace Distributions . [Thesis]. University of Guelph; 2014. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8006

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Guelph

12. Dang, Utkarsh. Mixtures of Power Exponential Distributions and Topics in Regression-based Mixture Models .

Degree: 2014, University of Guelph

 Mixture models continue to be the dominant framework for modelling heterogeneity in data. A family of mixtures of multivariate exponential power distributions that can robustly… (more)

Subjects/Keywords: model-based clustering; mixture models; EM algorithm; competing risks

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APA (6th Edition):

Dang, U. (2014). Mixtures of Power Exponential Distributions and Topics in Regression-based Mixture Models . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8089

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Dang, Utkarsh. “Mixtures of Power Exponential Distributions and Topics in Regression-based Mixture Models .” 2014. Thesis, University of Guelph. Accessed November 22, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8089.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Dang, Utkarsh. “Mixtures of Power Exponential Distributions and Topics in Regression-based Mixture Models .” 2014. Web. 22 Nov 2019.

Vancouver:

Dang U. Mixtures of Power Exponential Distributions and Topics in Regression-based Mixture Models . [Internet] [Thesis]. University of Guelph; 2014. [cited 2019 Nov 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8089.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Dang U. Mixtures of Power Exponential Distributions and Topics in Regression-based Mixture Models . [Thesis]. University of Guelph; 2014. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8089

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Guelph

13. Morris, Katherine. Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions .

Degree: 2012, University of Guelph

 We introduce a dimension reduction method for model-based clustering obtained from a finite mixture of t-distributions. This approach is based on existing work on reducing… (more)

Subjects/Keywords: mclust; tEIGEN; model-based; clustering; dimension; reduction; multivariate; t-mixtures

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APA (6th Edition):

Morris, K. (2012). Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3863

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Morris, Katherine. “Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions .” 2012. Thesis, University of Guelph. Accessed November 22, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3863.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Morris, Katherine. “Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions .” 2012. Web. 22 Nov 2019.

Vancouver:

Morris K. Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions . [Internet] [Thesis]. University of Guelph; 2012. [cited 2019 Nov 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3863.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Morris K. Dimension Reduction for Model-based Clustering via Mixtures of Multivariate t-Distributions . [Thesis]. University of Guelph; 2012. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3863

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Guelph

14. Murray, Paula. Mixtures of Skew-t Factor Analyzers .

Degree: 2012, University of Guelph

Model-based clustering allows for the identification of subgroups in a data set through the use of finite mixture models. When applied to high-dimensional microarray data,… (more)

Subjects/Keywords: Cluster Analysis; Model-based Clustering; Skew-t Distribution; Factor Analysis

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APA (6th Edition):

Murray, P. (2012). Mixtures of Skew-t Factor Analyzers . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/5274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Murray, Paula. “Mixtures of Skew-t Factor Analyzers .” 2012. Thesis, University of Guelph. Accessed November 22, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/5274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Murray, Paula. “Mixtures of Skew-t Factor Analyzers .” 2012. Web. 22 Nov 2019.

Vancouver:

Murray P. Mixtures of Skew-t Factor Analyzers . [Internet] [Thesis]. University of Guelph; 2012. [cited 2019 Nov 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/5274.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Murray P. Mixtures of Skew-t Factor Analyzers . [Thesis]. University of Guelph; 2012. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/5274

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


Colorado State University

15. Wang, Lulu. Some topics on model-based clustering.

Degree: PhD, Statistics, 2017, Colorado State University

 Cluster analysis is widely applied in various areas. Model-based clustering, which assumes a mixture model, is one of the most useful approaches in clustering. Using… (more)

Subjects/Keywords: model-based clustering; semiparametric; non-Gaussian; high-dimensional

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APA (6th Edition):

Wang, L. (2017). Some topics on model-based clustering. (Doctoral Dissertation). Colorado State University. Retrieved from http://hdl.handle.net/10217/178931

Chicago Manual of Style (16th Edition):

Wang, Lulu. “Some topics on model-based clustering.” 2017. Doctoral Dissertation, Colorado State University. Accessed November 22, 2019. http://hdl.handle.net/10217/178931.

MLA Handbook (7th Edition):

Wang, Lulu. “Some topics on model-based clustering.” 2017. Web. 22 Nov 2019.

Vancouver:

Wang L. Some topics on model-based clustering. [Internet] [Doctoral dissertation]. Colorado State University; 2017. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/10217/178931.

Council of Science Editors:

Wang L. Some topics on model-based clustering. [Doctoral Dissertation]. Colorado State University; 2017. Available from: http://hdl.handle.net/10217/178931


University of Washington

16. Gruhl, Jonathan. Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data.

Degree: PhD, 2012, University of Washington

 This dissertation studies parametric and semiparametric approaches to latent variable models, multivariate regression and model-based clustering for mixed outcomes. We use the term mixed outcomes… (more)

Subjects/Keywords: Bayesian Modeling; Latent Variable Model; Mixed Outcomes; Model-Based Clustering; Semiparametric; Statistics; Statistics

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APA (6th Edition):

Gruhl, J. (2012). Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/20904

Chicago Manual of Style (16th Edition):

Gruhl, Jonathan. “Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data.” 2012. Doctoral Dissertation, University of Washington. Accessed November 22, 2019. http://hdl.handle.net/1773/20904.

MLA Handbook (7th Edition):

Gruhl, Jonathan. “Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data.” 2012. Web. 22 Nov 2019.

Vancouver:

Gruhl J. Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data. [Internet] [Doctoral dissertation]. University of Washington; 2012. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/1773/20904.

Council of Science Editors:

Gruhl J. Bayesian Modeling For Multivariate Mixed Outcomes With Applications To Cognitive Testing Data. [Doctoral Dissertation]. University of Washington; 2012. Available from: http://hdl.handle.net/1773/20904


McMaster University

17. Wei, Yuhong. Extending Growth Mixture Models and Handling Missing Values via Mixtures of Non-Elliptical Distributions.

Degree: PhD, 2017, McMaster University

Growth mixture models (GMMs) are used to model intra-individual change and inter-individual differences in change and to detect underlying group structure in longitudinal studies. Regularly,… (more)

Subjects/Keywords: Growth Mixture Model; Model-Based Clustering; EM Algorithm; Missing Data; Finite Mixture Models

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wei, Y. (2017). Extending Growth Mixture Models and Handling Missing Values via Mixtures of Non-Elliptical Distributions. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/21987

Chicago Manual of Style (16th Edition):

Wei, Yuhong. “Extending Growth Mixture Models and Handling Missing Values via Mixtures of Non-Elliptical Distributions.” 2017. Doctoral Dissertation, McMaster University. Accessed November 22, 2019. http://hdl.handle.net/11375/21987.

MLA Handbook (7th Edition):

Wei, Yuhong. “Extending Growth Mixture Models and Handling Missing Values via Mixtures of Non-Elliptical Distributions.” 2017. Web. 22 Nov 2019.

Vancouver:

Wei Y. Extending Growth Mixture Models and Handling Missing Values via Mixtures of Non-Elliptical Distributions. [Internet] [Doctoral dissertation]. McMaster University; 2017. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/11375/21987.

Council of Science Editors:

Wei Y. Extending Growth Mixture Models and Handling Missing Values via Mixtures of Non-Elliptical Distributions. [Doctoral Dissertation]. McMaster University; 2017. Available from: http://hdl.handle.net/11375/21987


Indian Institute of Science

18. Milind, R. Clustering for Model Reduction of Circuits : Multi-level Techniques.

Degree: 2014, Indian Institute of Science

 Miniaturisation of electronic chips poses challenges at the design stage. The progressively decreasing circuit dimensions result in complex electrical behaviour that necessitates complex models. Simulation… (more)

Subjects/Keywords: MOR; Model Order Reduction; Clustering based Model Reduction; Model Order Reduction Algorithms; PRIMA Clustering Model Reduction; Linear Circuits -; Electronic Circuits; Krylov-subspace Methods; Model Reduction; Electronic Engineering

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Milind, R. (2014). Clustering for Model Reduction of Circuits : Multi-level Techniques. (Thesis). Indian Institute of Science. Retrieved from http://hdl.handle.net/2005/2774

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Milind, R. “Clustering for Model Reduction of Circuits : Multi-level Techniques.” 2014. Thesis, Indian Institute of Science. Accessed November 22, 2019. http://hdl.handle.net/2005/2774.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Milind, R. “Clustering for Model Reduction of Circuits : Multi-level Techniques.” 2014. Web. 22 Nov 2019.

Vancouver:

Milind R. Clustering for Model Reduction of Circuits : Multi-level Techniques. [Internet] [Thesis]. Indian Institute of Science; 2014. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/2005/2774.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Milind R. Clustering for Model Reduction of Circuits : Multi-level Techniques. [Thesis]. Indian Institute of Science; 2014. Available from: http://hdl.handle.net/2005/2774

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Guelph

19. Wei, Yuhong. Mixture Model Averaging for Clustering .

Degree: 2012, University of Guelph

Model-based clustering is based on a finite mixture of distributions, where each mixture component corresponds to a different group, cluster, subpopulation, or part thereof. Gaussian… (more)

Subjects/Keywords: mclust; merging mixture component; mixture model; model averaging; Model selection; model-based clustering; parameter estimation; pgmm; adjusted Rand index

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wei, Y. (2012). Mixture Model Averaging for Clustering . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3538

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Wei, Yuhong. “Mixture Model Averaging for Clustering .” 2012. Thesis, University of Guelph. Accessed November 22, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3538.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Wei, Yuhong. “Mixture Model Averaging for Clustering .” 2012. Web. 22 Nov 2019.

Vancouver:

Wei Y. Mixture Model Averaging for Clustering . [Internet] [Thesis]. University of Guelph; 2012. [cited 2019 Nov 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3538.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Wei Y. Mixture Model Averaging for Clustering . [Thesis]. University of Guelph; 2012. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/3538

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Guelph

20. Vrbik, Irene. Non-Elliptical and Fractionally-Supervised Classification .

Degree: 2014, University of Guelph

Model-based classification uses finite mixture models to infer a group structure in data. Three species of classification include unsupervised classification (cluster- ing), semi-supervised classification (or… (more)

Subjects/Keywords: finite mixture models; skew normal distribution skew-t distribution; EM algorithm; weighted likelihood; model-based classification; model-based clustering; discriminant analysis

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APA (6th Edition):

Vrbik, I. (2014). Non-Elliptical and Fractionally-Supervised Classification . (Thesis). University of Guelph. Retrieved from https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8096

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Vrbik, Irene. “Non-Elliptical and Fractionally-Supervised Classification .” 2014. Thesis, University of Guelph. Accessed November 22, 2019. https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8096.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Vrbik, Irene. “Non-Elliptical and Fractionally-Supervised Classification .” 2014. Web. 22 Nov 2019.

Vancouver:

Vrbik I. Non-Elliptical and Fractionally-Supervised Classification . [Internet] [Thesis]. University of Guelph; 2014. [cited 2019 Nov 22]. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8096.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Vrbik I. Non-Elliptical and Fractionally-Supervised Classification . [Thesis]. University of Guelph; 2014. Available from: https://atrium.lib.uoguelph.ca/xmlui/handle/10214/8096

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


NSYSU

21. Jan, Yi-An. Model-Based Clustering for Gene Expression and Change Patterns.

Degree: Master, Applied Mathematics, 2011, NSYSU

 It is important to study gene expression and change patterns over a time period because biologically related gene groups are likely to share similar patterns.… (more)

Subjects/Keywords: Gene expression; Model-based clustering; Wavelet coefficients; Fourier coefficients; Yeast cell cycle data

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Jan, Y. (2011). Model-Based Clustering for Gene Expression and Change Patterns. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729111-155816

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Jan, Yi-An. “Model-Based Clustering for Gene Expression and Change Patterns.” 2011. Thesis, NSYSU. Accessed November 22, 2019. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729111-155816.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Jan, Yi-An. “Model-Based Clustering for Gene Expression and Change Patterns.” 2011. Web. 22 Nov 2019.

Vancouver:

Jan Y. Model-Based Clustering for Gene Expression and Change Patterns. [Internet] [Thesis]. NSYSU; 2011. [cited 2019 Nov 22]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729111-155816.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Jan Y. Model-Based Clustering for Gene Expression and Change Patterns. [Thesis]. NSYSU; 2011. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0729111-155816

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Southern California

22. Cho, Yoon-Sik. Modeling and predicting with spatial‐temporal social networks.

Degree: PhD, Electrical Engineering, 2014, University of Southern California

 Network data and user behavior data are becoming pervasive. In this thesis, we develop efficient machine learning methods for addressing various real‐world problems using these… (more)

Subjects/Keywords: clustering; location based social networks; mixed membership; point processes; spatial‐temporal; topic model

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APA (6th Edition):

Cho, Y. (2014). Modeling and predicting with spatial‐temporal social networks. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/447544/rec/4118

Chicago Manual of Style (16th Edition):

Cho, Yoon-Sik. “Modeling and predicting with spatial‐temporal social networks.” 2014. Doctoral Dissertation, University of Southern California. Accessed November 22, 2019. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/447544/rec/4118.

MLA Handbook (7th Edition):

Cho, Yoon-Sik. “Modeling and predicting with spatial‐temporal social networks.” 2014. Web. 22 Nov 2019.

Vancouver:

Cho Y. Modeling and predicting with spatial‐temporal social networks. [Internet] [Doctoral dissertation]. University of Southern California; 2014. [cited 2019 Nov 22]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/447544/rec/4118.

Council of Science Editors:

Cho Y. Modeling and predicting with spatial‐temporal social networks. [Doctoral Dissertation]. University of Southern California; 2014. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/447544/rec/4118


IUPUI

23. Ara, Lena. Integrate Model and Instance Based Machine Learning for Network Intrusion Detection.

Degree: 2018, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

In computer networks, the convenient internet access facilitates internet services, but at the same time also augments the spread of… (more)

Subjects/Keywords: Intrusion Detection System; Clustering; Machine Learning; Botnet Traffic; Model and Instance Based

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APA (6th Edition):

Ara, L. (2018). Integrate Model and Instance Based Machine Learning for Network Intrusion Detection. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/17958

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Ara, Lena. “Integrate Model and Instance Based Machine Learning for Network Intrusion Detection.” 2018. Thesis, IUPUI. Accessed November 22, 2019. http://hdl.handle.net/1805/17958.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Ara, Lena. “Integrate Model and Instance Based Machine Learning for Network Intrusion Detection.” 2018. Web. 22 Nov 2019.

Vancouver:

Ara L. Integrate Model and Instance Based Machine Learning for Network Intrusion Detection. [Internet] [Thesis]. IUPUI; 2018. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/1805/17958.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Ara L. Integrate Model and Instance Based Machine Learning for Network Intrusion Detection. [Thesis]. IUPUI; 2018. Available from: http://hdl.handle.net/1805/17958

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Washington

24. Young, William Chad. Bayesian Methods for Inferring Gene Regulatory Networks.

Degree: PhD, 2016, University of Washington

 The recent explosion in the availability of gene expression data has opened up new possibilities in advancing our understanding of the fundamental processes of life.… (more)

Subjects/Keywords: Bayesian statistics; gene regulatory network; LINCS; model-based clustering; Statistics; Genetics; statistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Young, W. C. (2016). Bayesian Methods for Inferring Gene Regulatory Networks. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/37254

Chicago Manual of Style (16th Edition):

Young, William Chad. “Bayesian Methods for Inferring Gene Regulatory Networks.” 2016. Doctoral Dissertation, University of Washington. Accessed November 22, 2019. http://hdl.handle.net/1773/37254.

MLA Handbook (7th Edition):

Young, William Chad. “Bayesian Methods for Inferring Gene Regulatory Networks.” 2016. Web. 22 Nov 2019.

Vancouver:

Young WC. Bayesian Methods for Inferring Gene Regulatory Networks. [Internet] [Doctoral dissertation]. University of Washington; 2016. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/1773/37254.

Council of Science Editors:

Young WC. Bayesian Methods for Inferring Gene Regulatory Networks. [Doctoral Dissertation]. University of Washington; 2016. Available from: http://hdl.handle.net/1773/37254


McMaster University

25. Cheam, Amay SM. Mixture models for ROC curve and spatio-temporal clustering.

Degree: PhD, 2016, McMaster University

Finite mixture models have had a profound impact on the history of statistics, contributing to modelling heterogeneous populations, generalizing distributional assumptions, and lately, presenting a… (more)

Subjects/Keywords: Finite mixture models; ROC curve; Spatio-temporal data; Functional data; Model-based clustering; EM algorithm

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Cheam, A. S. (2016). Mixture models for ROC curve and spatio-temporal clustering. (Doctoral Dissertation). McMaster University. Retrieved from http://hdl.handle.net/11375/20693

Chicago Manual of Style (16th Edition):

Cheam, Amay SM. “Mixture models for ROC curve and spatio-temporal clustering.” 2016. Doctoral Dissertation, McMaster University. Accessed November 22, 2019. http://hdl.handle.net/11375/20693.

MLA Handbook (7th Edition):

Cheam, Amay SM. “Mixture models for ROC curve and spatio-temporal clustering.” 2016. Web. 22 Nov 2019.

Vancouver:

Cheam AS. Mixture models for ROC curve and spatio-temporal clustering. [Internet] [Doctoral dissertation]. McMaster University; 2016. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/11375/20693.

Council of Science Editors:

Cheam AS. Mixture models for ROC curve and spatio-temporal clustering. [Doctoral Dissertation]. McMaster University; 2016. Available from: http://hdl.handle.net/11375/20693


University of Tennessee – Knoxville

26. Erar, Bahar. Mixture model cluster analysis under different covariance structures using information complexity.

Degree: MS, Statistics, 2011, University of Tennessee – Knoxville

  In this thesis, a mixture-model cluster analysis technique under different covariance structures of the component densities is developed and presented, to capture the compactness,… (more)

Subjects/Keywords: Gaussian mixture; model-based clustering; information complexity; Gibbs sampler; eigenvalue decomposition; Multivariate Analysis; Statistical Models

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APA (6th Edition):

Erar, B. (2011). Mixture model cluster analysis under different covariance structures using information complexity. (Thesis). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_gradthes/968

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Erar, Bahar. “Mixture model cluster analysis under different covariance structures using information complexity.” 2011. Thesis, University of Tennessee – Knoxville. Accessed November 22, 2019. https://trace.tennessee.edu/utk_gradthes/968.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Erar, Bahar. “Mixture model cluster analysis under different covariance structures using information complexity.” 2011. Web. 22 Nov 2019.

Vancouver:

Erar B. Mixture model cluster analysis under different covariance structures using information complexity. [Internet] [Thesis]. University of Tennessee – Knoxville; 2011. [cited 2019 Nov 22]. Available from: https://trace.tennessee.edu/utk_gradthes/968.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Erar B. Mixture model cluster analysis under different covariance structures using information complexity. [Thesis]. University of Tennessee – Knoxville; 2011. Available from: https://trace.tennessee.edu/utk_gradthes/968

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation


University of Washington

27. Wan, Yali. Topics in Graph Clustering.

Degree: PhD, 2017, University of Washington

 In this thesis, two problems in social networks will be studied. In the first part of the thesis, we focus on community recovery problems for… (more)

Subjects/Keywords: community recovery; graph clustering; graph perturbation; model-based; model-free; robustness of graph properties; Statistics; Mathematics; Computer science; Statistics

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Wan, Y. (2017). Topics in Graph Clustering. (Doctoral Dissertation). University of Washington. Retrieved from http://hdl.handle.net/1773/40717

Chicago Manual of Style (16th Edition):

Wan, Yali. “Topics in Graph Clustering.” 2017. Doctoral Dissertation, University of Washington. Accessed November 22, 2019. http://hdl.handle.net/1773/40717.

MLA Handbook (7th Edition):

Wan, Yali. “Topics in Graph Clustering.” 2017. Web. 22 Nov 2019.

Vancouver:

Wan Y. Topics in Graph Clustering. [Internet] [Doctoral dissertation]. University of Washington; 2017. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/1773/40717.

Council of Science Editors:

Wan Y. Topics in Graph Clustering. [Doctoral Dissertation]. University of Washington; 2017. Available from: http://hdl.handle.net/1773/40717

28. Kuruppumullage Don, Prabhani. EVOLUTION CHARACTERISTICS OF ENSEMBLE FORECASTS THROUGH.

Degree: MS, Statistics, 2011, Penn State University

 Tropical and extra-tropical cyclones pose considerable societal risks, especially to the increasing populations living near the coasts. As a result, they have been studied since… (more)

Subjects/Keywords: ensemble forecasts; path clustering; Model-based clustering; cyclones

…cluster solution. With model-based clustering, more formal procedures have been developed to… …2.2 and 3.2. For any clustering method (including model-based clustering) it is… …evaluate the quality of clustering solutions in a model-based clustering setup; that is to… …A detailed description is provided in chapter 2. Of course for model-based clustering… …x5B;16]). In this thesis we will look at how model-based clustering can be used to… 

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APA (6th Edition):

Kuruppumullage Don, P. (2011). EVOLUTION CHARACTERISTICS OF ENSEMBLE FORECASTS THROUGH. (Masters Thesis). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/11793

Chicago Manual of Style (16th Edition):

Kuruppumullage Don, Prabhani. “EVOLUTION CHARACTERISTICS OF ENSEMBLE FORECASTS THROUGH.” 2011. Masters Thesis, Penn State University. Accessed November 22, 2019. https://etda.libraries.psu.edu/catalog/11793.

MLA Handbook (7th Edition):

Kuruppumullage Don, Prabhani. “EVOLUTION CHARACTERISTICS OF ENSEMBLE FORECASTS THROUGH.” 2011. Web. 22 Nov 2019.

Vancouver:

Kuruppumullage Don P. EVOLUTION CHARACTERISTICS OF ENSEMBLE FORECASTS THROUGH. [Internet] [Masters thesis]. Penn State University; 2011. [cited 2019 Nov 22]. Available from: https://etda.libraries.psu.edu/catalog/11793.

Council of Science Editors:

Kuruppumullage Don P. EVOLUTION CHARACTERISTICS OF ENSEMBLE FORECASTS THROUGH. [Masters Thesis]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/11793

29. Lafond Lapalme, Joël. Étude et décontamination du transcriptome de novo du nématode doré Globodera rostochiensis.

Degree: M. Sc., Biologie, 2016, Université de Sherbrooke

 Le nématode doré, Globodera rostochiensis, est un nématode phytoparasite qui peut infecter des plantes agricoles telles la pomme de terre, la tomate et l’aubergine. En… (more)

Subjects/Keywords: Nématode doré; Globodera rostochiensis; Éclosion; Transcriptome; Assemblage de novo; Gènes différentiellement exprimés; Décontamination; Model-based Categorical Sequence Clustering; MCSC

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APA (6th Edition):

Lafond Lapalme, J. (2016). Étude et décontamination du transcriptome de novo du nématode doré Globodera rostochiensis. (Masters Thesis). Université de Sherbrooke. Retrieved from http://www.collectionscanada.gc.ca/obj/thesescanada/vol2/QSHERU/TC-QSHERU-11143_8882.pdf ; http://savoirs.usherbrooke.ca/bitstream/11143/8882/6/Lafond_Lapalme_Joel_MSc_2016.pdf

Chicago Manual of Style (16th Edition):

Lafond Lapalme, Joël. “Étude et décontamination du transcriptome de novo du nématode doré Globodera rostochiensis.” 2016. Masters Thesis, Université de Sherbrooke. Accessed November 22, 2019. http://www.collectionscanada.gc.ca/obj/thesescanada/vol2/QSHERU/TC-QSHERU-11143_8882.pdf ; http://savoirs.usherbrooke.ca/bitstream/11143/8882/6/Lafond_Lapalme_Joel_MSc_2016.pdf.

MLA Handbook (7th Edition):

Lafond Lapalme, Joël. “Étude et décontamination du transcriptome de novo du nématode doré Globodera rostochiensis.” 2016. Web. 22 Nov 2019.

Vancouver:

Lafond Lapalme J. Étude et décontamination du transcriptome de novo du nématode doré Globodera rostochiensis. [Internet] [Masters thesis]. Université de Sherbrooke; 2016. [cited 2019 Nov 22]. Available from: http://www.collectionscanada.gc.ca/obj/thesescanada/vol2/QSHERU/TC-QSHERU-11143_8882.pdf ; http://savoirs.usherbrooke.ca/bitstream/11143/8882/6/Lafond_Lapalme_Joel_MSc_2016.pdf.

Council of Science Editors:

Lafond Lapalme J. Étude et décontamination du transcriptome de novo du nématode doré Globodera rostochiensis. [Masters Thesis]. Université de Sherbrooke; 2016. Available from: http://www.collectionscanada.gc.ca/obj/thesescanada/vol2/QSHERU/TC-QSHERU-11143_8882.pdf ; http://savoirs.usherbrooke.ca/bitstream/11143/8882/6/Lafond_Lapalme_Joel_MSc_2016.pdf


Georgia Tech

30. Jiang, Huijing. Statistical computation and inference for functional data analysis.

Degree: PhD, Industrial and Systems Engineering, 2010, Georgia Tech

 My doctoral research dissertation focuses on two aspects of functional data analysis (FDA): FDA under spatial interdependence and FDA for multi-level data. The first part… (more)

Subjects/Keywords: Service distribution equity; Multi-level data; Model-based clustering; Spatio-temporal; Functional data analysis; Multilevel models (Statistics); Markov random fields

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APA (6th Edition):

Jiang, H. (2010). Statistical computation and inference for functional data analysis. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/37087

Chicago Manual of Style (16th Edition):

Jiang, Huijing. “Statistical computation and inference for functional data analysis.” 2010. Doctoral Dissertation, Georgia Tech. Accessed November 22, 2019. http://hdl.handle.net/1853/37087.

MLA Handbook (7th Edition):

Jiang, Huijing. “Statistical computation and inference for functional data analysis.” 2010. Web. 22 Nov 2019.

Vancouver:

Jiang H. Statistical computation and inference for functional data analysis. [Internet] [Doctoral dissertation]. Georgia Tech; 2010. [cited 2019 Nov 22]. Available from: http://hdl.handle.net/1853/37087.

Council of Science Editors:

Jiang H. Statistical computation and inference for functional data analysis. [Doctoral Dissertation]. Georgia Tech; 2010. Available from: http://hdl.handle.net/1853/37087

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