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You searched for subject:(Co clustering). Showing records 1 – 30 of 46 total matches.

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Rochester Institute of Technology

1. Green, Nathan S. Evolutionary spectral co-clustering.

Degree: Computer Science (GCCIS), 2010, Rochester Institute of Technology

 The field of mining evolving data is relatively new and evolutionary clustering is among the latest in this trend. Presently, there are algorithms for evolutionary… (more)

Subjects/Keywords: Clustering; Co-clustering; Data mining; Evolving data; Spectral clustering

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APA (6th Edition):

Green, N. S. (2010). Evolutionary spectral co-clustering. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/673

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):

Green, Nathan S. “Evolutionary spectral co-clustering.” 2010. Thesis, Rochester Institute of Technology. Accessed February 25, 2020. https://scholarworks.rit.edu/theses/673.

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

MLA Handbook (7th Edition):

Green, Nathan S. “Evolutionary spectral co-clustering.” 2010. Web. 25 Feb 2020.

Vancouver:

Green NS. Evolutionary spectral co-clustering. [Internet] [Thesis]. Rochester Institute of Technology; 2010. [cited 2020 Feb 25]. Available from: https://scholarworks.rit.edu/theses/673.

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

Council of Science Editors:

Green NS. Evolutionary spectral co-clustering. [Thesis]. Rochester Institute of Technology; 2010. Available from: https://scholarworks.rit.edu/theses/673

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


University of Illinois – Urbana-Champaign

2. Liu, Yiyi. NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression.

Degree: MS, 0408, 2013, University of Illinois – Urbana-Champaign

 Cancer subtype information is critically important for designing more effective treatments. In this thesis, we introduce a new co-clustering algorithm for cancer subtype identification, which… (more)

Subjects/Keywords: Cancer Subtype; Co-clustering; Gene Expression

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APA (6th Edition):

Liu, Y. (2013). NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/44754

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):

Liu, Yiyi. “NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression.” 2013. Thesis, University of Illinois – Urbana-Champaign. Accessed February 25, 2020. http://hdl.handle.net/2142/44754.

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

MLA Handbook (7th Edition):

Liu, Yiyi. “NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression.” 2013. Web. 25 Feb 2020.

Vancouver:

Liu Y. NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2013. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2142/44754.

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

Council of Science Editors:

Liu Y. NCIS: a network-assisted co-clustering algorithm to discover cancer subtypes based on gene expression. [Thesis]. University of Illinois – Urbana-Champaign; 2013. Available from: http://hdl.handle.net/2142/44754

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

3. Guigourès, Romain. Utilisation des modèles de co-clustering pour l'analyse exploratoire des données : No English title available.

Degree: Docteur es, Mathématiques appliquées, 2013, Paris 1

Le co-clustering est une technique de classification consistant à réaliser une partition simultanée des lignes et des colonnes d’une matrice de données. Parmi les approches… (more)

Subjects/Keywords: Co-clustering; MODL; Graphes; Données volumineuses; Co-clustering; MODL; Graphs partitioning; 519

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APA (6th Edition):

Guigourès, R. (2013). Utilisation des modèles de co-clustering pour l'analyse exploratoire des données : No English title available. (Doctoral Dissertation). Paris 1. Retrieved from http://www.theses.fr/2013PA010070

Chicago Manual of Style (16th Edition):

Guigourès, Romain. “Utilisation des modèles de co-clustering pour l'analyse exploratoire des données : No English title available.” 2013. Doctoral Dissertation, Paris 1. Accessed February 25, 2020. http://www.theses.fr/2013PA010070.

MLA Handbook (7th Edition):

Guigourès, Romain. “Utilisation des modèles de co-clustering pour l'analyse exploratoire des données : No English title available.” 2013. Web. 25 Feb 2020.

Vancouver:

Guigourès R. Utilisation des modèles de co-clustering pour l'analyse exploratoire des données : No English title available. [Internet] [Doctoral dissertation]. Paris 1; 2013. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2013PA010070.

Council of Science Editors:

Guigourès R. Utilisation des modèles de co-clustering pour l'analyse exploratoire des données : No English title available. [Doctoral Dissertation]. Paris 1; 2013. Available from: http://www.theses.fr/2013PA010070

4. -7585-6925. Distributed and dynamic factor modeling of online data.

Degree: PhD, Electrical and Computer Engineering, 2017, University of Texas – Austin

 The domain of data mining and machine learning has expanded rapidly in recent years to include both large-scale distributed and streaming computation. Although many open-source… (more)

Subjects/Keywords: Distributed clustering; Dynamic clustering; Matrix factorization; Co-factorization

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APA (6th Edition):

-7585-6925. (2017). Distributed and dynamic factor modeling of online data. (Doctoral Dissertation). University of Texas – Austin. Retrieved from http://hdl.handle.net/2152/62065

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Chicago Manual of Style (16th Edition):

-7585-6925. “Distributed and dynamic factor modeling of online data.” 2017. Doctoral Dissertation, University of Texas – Austin. Accessed February 25, 2020. http://hdl.handle.net/2152/62065.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

MLA Handbook (7th Edition):

-7585-6925. “Distributed and dynamic factor modeling of online data.” 2017. Web. 25 Feb 2020.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Vancouver:

-7585-6925. Distributed and dynamic factor modeling of online data. [Internet] [Doctoral dissertation]. University of Texas – Austin; 2017. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2152/62065.

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete

Council of Science Editors:

-7585-6925. Distributed and dynamic factor modeling of online data. [Doctoral Dissertation]. University of Texas – Austin; 2017. Available from: http://hdl.handle.net/2152/62065

Note: this citation may be lacking information needed for this citation format:
Author name may be incomplete


Rochester Institute of Technology

5. Salunke, Amit. Evolutionary star-structured heterogeneous data co-clustering.

Degree: Computer Science (GCCIS), 2012, Rochester Institute of Technology

 A star-structured interrelationship, which is a more common type in real world data, has a central object connected to the other types of objects. One… (more)

Subjects/Keywords: Clustering; Co-clustering; Evolutionary; Heterogeneous data; Non-negative matrix factorization; Star-structured

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APA (6th Edition):

Salunke, A. (2012). Evolutionary star-structured heterogeneous data co-clustering. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/5522

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):

Salunke, Amit. “Evolutionary star-structured heterogeneous data co-clustering.” 2012. Thesis, Rochester Institute of Technology. Accessed February 25, 2020. https://scholarworks.rit.edu/theses/5522.

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

MLA Handbook (7th Edition):

Salunke, Amit. “Evolutionary star-structured heterogeneous data co-clustering.” 2012. Web. 25 Feb 2020.

Vancouver:

Salunke A. Evolutionary star-structured heterogeneous data co-clustering. [Internet] [Thesis]. Rochester Institute of Technology; 2012. [cited 2020 Feb 25]. Available from: https://scholarworks.rit.edu/theses/5522.

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

Council of Science Editors:

Salunke A. Evolutionary star-structured heterogeneous data co-clustering. [Thesis]. Rochester Institute of Technology; 2012. Available from: https://scholarworks.rit.edu/theses/5522

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


Temple University

6. Barnathan, Michael. Mining Complex High-Order Datasets.

Degree: PhD, 2010, Temple University

Computer and Information Science

Selection of an appropriate structure for storage and analysis of complex datasets is a vital but often overlooked decision in the… (more)

Subjects/Keywords: Health Sciences, Radiology; Co-clustering; Data mining; fMRI; SVD; Tensors; Wavelets

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APA (6th Edition):

Barnathan, M. (2010). Mining Complex High-Order Datasets. (Doctoral Dissertation). Temple University. Retrieved from http://digital.library.temple.edu/u?/p245801coll10,82058

Chicago Manual of Style (16th Edition):

Barnathan, Michael. “Mining Complex High-Order Datasets.” 2010. Doctoral Dissertation, Temple University. Accessed February 25, 2020. http://digital.library.temple.edu/u?/p245801coll10,82058.

MLA Handbook (7th Edition):

Barnathan, Michael. “Mining Complex High-Order Datasets.” 2010. Web. 25 Feb 2020.

Vancouver:

Barnathan M. Mining Complex High-Order Datasets. [Internet] [Doctoral dissertation]. Temple University; 2010. [cited 2020 Feb 25]. Available from: http://digital.library.temple.edu/u?/p245801coll10,82058.

Council of Science Editors:

Barnathan M. Mining Complex High-Order Datasets. [Doctoral Dissertation]. Temple University; 2010. Available from: http://digital.library.temple.edu/u?/p245801coll10,82058


The Ohio State University

7. Ramanathan, Venkatram. Parallelizing Applications With a Reduction Based Framework on Multi-Core Clusters.

Degree: MS, Computer Science and Engineering, 2010, The Ohio State University

 Data mining has emerged as an important class of high performance applications. Atthe same time, most parallel platforms today are clusters of multi-core machines. Thus,… (more)

Subjects/Keywords: Computer Science; Parallel wavelet transform; parallel co-clustering; reduction based framework

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APA (6th Edition):

Ramanathan, V. (2010). Parallelizing Applications With a Reduction Based Framework on Multi-Core Clusters. (Masters Thesis). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1275337592

Chicago Manual of Style (16th Edition):

Ramanathan, Venkatram. “Parallelizing Applications With a Reduction Based Framework on Multi-Core Clusters.” 2010. Masters Thesis, The Ohio State University. Accessed February 25, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1275337592.

MLA Handbook (7th Edition):

Ramanathan, Venkatram. “Parallelizing Applications With a Reduction Based Framework on Multi-Core Clusters.” 2010. Web. 25 Feb 2020.

Vancouver:

Ramanathan V. Parallelizing Applications With a Reduction Based Framework on Multi-Core Clusters. [Internet] [Masters thesis]. The Ohio State University; 2010. [cited 2020 Feb 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1275337592.

Council of Science Editors:

Ramanathan V. Parallelizing Applications With a Reduction Based Framework on Multi-Core Clusters. [Masters Thesis]. The Ohio State University; 2010. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1275337592


Universiteit Utrecht

8. Garcia Lopes Maia Rodrigues, J. Computational Structural Biology of Macromolecular Interactions.

Degree: 2014, Universiteit Utrecht

 The living cell is a formidable entity kept intact and functioning by a network of interactions carried out by protein molecules. As such, understanding this… (more)

Subjects/Keywords: protein docking; HADDOCK; integrative modeling; co-evolution; clustering; protein interactions

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APA (6th Edition):

Garcia Lopes Maia Rodrigues, J. (2014). Computational Structural Biology of Macromolecular Interactions. (Doctoral Dissertation). Universiteit Utrecht. Retrieved from http://dspace.library.uu.nl:8080/handle/1874/307246

Chicago Manual of Style (16th Edition):

Garcia Lopes Maia Rodrigues, J. “Computational Structural Biology of Macromolecular Interactions.” 2014. Doctoral Dissertation, Universiteit Utrecht. Accessed February 25, 2020. http://dspace.library.uu.nl:8080/handle/1874/307246.

MLA Handbook (7th Edition):

Garcia Lopes Maia Rodrigues, J. “Computational Structural Biology of Macromolecular Interactions.” 2014. Web. 25 Feb 2020.

Vancouver:

Garcia Lopes Maia Rodrigues J. Computational Structural Biology of Macromolecular Interactions. [Internet] [Doctoral dissertation]. Universiteit Utrecht; 2014. [cited 2020 Feb 25]. Available from: http://dspace.library.uu.nl:8080/handle/1874/307246.

Council of Science Editors:

Garcia Lopes Maia Rodrigues J. Computational Structural Biology of Macromolecular Interactions. [Doctoral Dissertation]. Universiteit Utrecht; 2014. Available from: http://dspace.library.uu.nl:8080/handle/1874/307246

9. Wang, Xinyu. Toward Scalable Hierarchical Clustering and Co-clustering Methods : application to the Cluster Hypothesis in Information Retrieval : Méthodes de regroupement hiérarchique agglomératif et co-clustering, leurs applications aux tests d’hypothèse de cluster et implémentations distribuées.

Degree: Docteur es, Informatique, 2017, Lyon

Comme une méthode d’apprentissage automatique non supervisé, la classification automatique est largement appliquée dans des tâches diverses. Différentes méthodes de la classification ont leurs caractéristiques… (more)

Subjects/Keywords: Classification ascendante hiérarchique; Co-clustering; Recherche d’informations; Hypothèse de cluster; Calcul distribué; Hierarchical clustering; Co-clustering; Information Retrieval; Cluster hypothesis; Distributed computing

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APA (6th Edition):

Wang, X. (2017). Toward Scalable Hierarchical Clustering and Co-clustering Methods : application to the Cluster Hypothesis in Information Retrieval : Méthodes de regroupement hiérarchique agglomératif et co-clustering, leurs applications aux tests d’hypothèse de cluster et implémentations distribuées. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2017LYSE2123

Chicago Manual of Style (16th Edition):

Wang, Xinyu. “Toward Scalable Hierarchical Clustering and Co-clustering Methods : application to the Cluster Hypothesis in Information Retrieval : Méthodes de regroupement hiérarchique agglomératif et co-clustering, leurs applications aux tests d’hypothèse de cluster et implémentations distribuées.” 2017. Doctoral Dissertation, Lyon. Accessed February 25, 2020. http://www.theses.fr/2017LYSE2123.

MLA Handbook (7th Edition):

Wang, Xinyu. “Toward Scalable Hierarchical Clustering and Co-clustering Methods : application to the Cluster Hypothesis in Information Retrieval : Méthodes de regroupement hiérarchique agglomératif et co-clustering, leurs applications aux tests d’hypothèse de cluster et implémentations distribuées.” 2017. Web. 25 Feb 2020.

Vancouver:

Wang X. Toward Scalable Hierarchical Clustering and Co-clustering Methods : application to the Cluster Hypothesis in Information Retrieval : Méthodes de regroupement hiérarchique agglomératif et co-clustering, leurs applications aux tests d’hypothèse de cluster et implémentations distribuées. [Internet] [Doctoral dissertation]. Lyon; 2017. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2017LYSE2123.

Council of Science Editors:

Wang X. Toward Scalable Hierarchical Clustering and Co-clustering Methods : application to the Cluster Hypothesis in Information Retrieval : Méthodes de regroupement hiérarchique agglomératif et co-clustering, leurs applications aux tests d’hypothèse de cluster et implémentations distribuées. [Doctoral Dissertation]. Lyon; 2017. Available from: http://www.theses.fr/2017LYSE2123


The Ohio State University

10. Bozdag, Doruk. Graph Coloring and Clustering Algorithms for Science and Engineering Applications.

Degree: PhD, Electrical and Computer Engineering, 2008, The Ohio State University

 In this dissertation, efficient algorithms are proposed for two important combinatorial problems in science and engineering applications: parallel graph coloring and subspace clustering. For parallel… (more)

Subjects/Keywords: Bioinformatics; Computer Science; Electrical Engineering; parallel graph coloring; distributed memor; y biclustering; co-clustering; subspace clustering

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APA (6th Edition):

Bozdag, D. (2008). Graph Coloring and Clustering Algorithms for Science and Engineering Applications. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1229459765

Chicago Manual of Style (16th Edition):

Bozdag, Doruk. “Graph Coloring and Clustering Algorithms for Science and Engineering Applications.” 2008. Doctoral Dissertation, The Ohio State University. Accessed February 25, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1229459765.

MLA Handbook (7th Edition):

Bozdag, Doruk. “Graph Coloring and Clustering Algorithms for Science and Engineering Applications.” 2008. Web. 25 Feb 2020.

Vancouver:

Bozdag D. Graph Coloring and Clustering Algorithms for Science and Engineering Applications. [Internet] [Doctoral dissertation]. The Ohio State University; 2008. [cited 2020 Feb 25]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1229459765.

Council of Science Editors:

Bozdag D. Graph Coloring and Clustering Algorithms for Science and Engineering Applications. [Doctoral Dissertation]. The Ohio State University; 2008. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1229459765

11. Médoc, Nicolas. A visual analytics approach for multi-resolution and multi-model analysis of text corpora : application to investigative journalism : Une approche de visualisation analytique pour une analyse multi-résolution de corpus textuels : application au journalisme d’investigation.

Degree: Docteur es, Informatique, 2017, Sorbonne Paris Cité

 À mesure que la production de textes numériques croît exponentiellement, un besoin grandissant d’analyser des corpus de textes se manifeste dans beaucoup de domaines d’application,… (more)

Subjects/Keywords: Visualisation analytique; Fouille de texte; Modèles de sujet; Co-clustering; Étude utilisateur; Journalisme d'investigation; Visual analytics; Text mining; Topic models; Co-clustering; User study; Investigative journalism; 005.74

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APA (6th Edition):

Médoc, N. (2017). A visual analytics approach for multi-resolution and multi-model analysis of text corpora : application to investigative journalism : Une approche de visualisation analytique pour une analyse multi-résolution de corpus textuels : application au journalisme d’investigation. (Doctoral Dissertation). Sorbonne Paris Cité. Retrieved from http://www.theses.fr/2017USPCB042

Chicago Manual of Style (16th Edition):

Médoc, Nicolas. “A visual analytics approach for multi-resolution and multi-model analysis of text corpora : application to investigative journalism : Une approche de visualisation analytique pour une analyse multi-résolution de corpus textuels : application au journalisme d’investigation.” 2017. Doctoral Dissertation, Sorbonne Paris Cité. Accessed February 25, 2020. http://www.theses.fr/2017USPCB042.

MLA Handbook (7th Edition):

Médoc, Nicolas. “A visual analytics approach for multi-resolution and multi-model analysis of text corpora : application to investigative journalism : Une approche de visualisation analytique pour une analyse multi-résolution de corpus textuels : application au journalisme d’investigation.” 2017. Web. 25 Feb 2020.

Vancouver:

Médoc N. A visual analytics approach for multi-resolution and multi-model analysis of text corpora : application to investigative journalism : Une approche de visualisation analytique pour une analyse multi-résolution de corpus textuels : application au journalisme d’investigation. [Internet] [Doctoral dissertation]. Sorbonne Paris Cité; 2017. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2017USPCB042.

Council of Science Editors:

Médoc N. A visual analytics approach for multi-resolution and multi-model analysis of text corpora : application to investigative journalism : Une approche de visualisation analytique pour une analyse multi-résolution de corpus textuels : application au journalisme d’investigation. [Doctoral Dissertation]. Sorbonne Paris Cité; 2017. Available from: http://www.theses.fr/2017USPCB042

12. Lee, En-Shiun Annie. Discovery and Analysis of Aligned Pattern Clusters from Protein Family Sequences.

Degree: 2014, University of Waterloo

 Protein sequences are essential for encoding molecular structures and functions. Consequently, biologists invest substantial resources and time discovering functional patterns in proteins. Using high-throughput technologies,… (more)

Subjects/Keywords: pattern discovery; knowledge discovery; clustering; protein; functionality; sequence pattern; co-occurrence; spectral clustering; hierarchical clustering; classification; unsupervised learning; cluster validity measure; Jaccard Index; Mutual Information; Information gain; pattern alignment

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APA (6th Edition):

Lee, E. A. (2014). Discovery and Analysis of Aligned Pattern Clusters from Protein Family Sequences. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/8365

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):

Lee, En-Shiun Annie. “Discovery and Analysis of Aligned Pattern Clusters from Protein Family Sequences.” 2014. Thesis, University of Waterloo. Accessed February 25, 2020. http://hdl.handle.net/10012/8365.

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

MLA Handbook (7th Edition):

Lee, En-Shiun Annie. “Discovery and Analysis of Aligned Pattern Clusters from Protein Family Sequences.” 2014. Web. 25 Feb 2020.

Vancouver:

Lee EA. Discovery and Analysis of Aligned Pattern Clusters from Protein Family Sequences. [Internet] [Thesis]. University of Waterloo; 2014. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/10012/8365.

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

Council of Science Editors:

Lee EA. Discovery and Analysis of Aligned Pattern Clusters from Protein Family Sequences. [Thesis]. University of Waterloo; 2014. Available from: http://hdl.handle.net/10012/8365

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

13. Sarkar, Chandrima. Improving Predictive Modeling in High Dimensional, Heterogeneous and Sparse Health Care Data.

Degree: PhD, Computer Science, 2015, University of Minnesota

 In the past few decades predictive modeling has emerged as an important tool for exploratory data analysis and decision making in health care. Predictive modeling… (more)

Subjects/Keywords: Co-clustering; Feature Selection; Health care; High Dimensional Data; Missing Value Imputation; Predictive Modeling

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APA (6th Edition):

Sarkar, C. (2015). Improving Predictive Modeling in High Dimensional, Heterogeneous and Sparse Health Care Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/175324

Chicago Manual of Style (16th Edition):

Sarkar, Chandrima. “Improving Predictive Modeling in High Dimensional, Heterogeneous and Sparse Health Care Data.” 2015. Doctoral Dissertation, University of Minnesota. Accessed February 25, 2020. http://hdl.handle.net/11299/175324.

MLA Handbook (7th Edition):

Sarkar, Chandrima. “Improving Predictive Modeling in High Dimensional, Heterogeneous and Sparse Health Care Data.” 2015. Web. 25 Feb 2020.

Vancouver:

Sarkar C. Improving Predictive Modeling in High Dimensional, Heterogeneous and Sparse Health Care Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2015. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/11299/175324.

Council of Science Editors:

Sarkar C. Improving Predictive Modeling in High Dimensional, Heterogeneous and Sparse Health Care Data. [Doctoral Dissertation]. University of Minnesota; 2015. Available from: http://hdl.handle.net/11299/175324


Wayne State University

14. Odibat, Omar. Differential modeling for cancer microarray data.

Degree: PhD, Computer Science, 2012, Wayne State University

  Capturing the changes between two biological phenotypes is a crucial task in understanding the mechanisms of various diseases. Most of the existing computational approaches… (more)

Subjects/Keywords: biclustering, cancer microarray, co-clustering, differential analysis, gene expression data, networks; Bioinformatics; Computer Sciences

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APA (6th Edition):

Odibat, O. (2012). Differential modeling for cancer microarray data. (Doctoral Dissertation). Wayne State University. Retrieved from https://digitalcommons.wayne.edu/oa_dissertations/578

Chicago Manual of Style (16th Edition):

Odibat, Omar. “Differential modeling for cancer microarray data.” 2012. Doctoral Dissertation, Wayne State University. Accessed February 25, 2020. https://digitalcommons.wayne.edu/oa_dissertations/578.

MLA Handbook (7th Edition):

Odibat, Omar. “Differential modeling for cancer microarray data.” 2012. Web. 25 Feb 2020.

Vancouver:

Odibat O. Differential modeling for cancer microarray data. [Internet] [Doctoral dissertation]. Wayne State University; 2012. [cited 2020 Feb 25]. Available from: https://digitalcommons.wayne.edu/oa_dissertations/578.

Council of Science Editors:

Odibat O. Differential modeling for cancer microarray data. [Doctoral Dissertation]. Wayne State University; 2012. Available from: https://digitalcommons.wayne.edu/oa_dissertations/578

15. Schmutz, Amandine. Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport : Contributions to the analysis of multivariate functional data, application to the study of the sport horse's locomotion.

Degree: Docteur es, Mathématiques, 2019, Lyon

Avec l'essor des objets connectés pour fournir un suivi systématique, objectif et fiable aux sportifs et à leur entraineur, de plus en plus de paramètres… (more)

Subjects/Keywords: Données fonctionnelles; Clustering; Co-clustering fonctionnel multivarié; Analyse en composantes principales fonctionnelle multivariée; Modèle à blocs latents; SEM-Gibbs; Algorithme EM; Functional data; Model based clustering; Multivariate functional principal component analysis; Latent block model; SEM-Gibbs; EM algorithm; Multivariate functional co-clustering; 510

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

APA (6th Edition):

Schmutz, A. (2019). Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport : Contributions to the analysis of multivariate functional data, application to the study of the sport horse's locomotion. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2019LYSE1241

Chicago Manual of Style (16th Edition):

Schmutz, Amandine. “Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport : Contributions to the analysis of multivariate functional data, application to the study of the sport horse's locomotion.” 2019. Doctoral Dissertation, Lyon. Accessed February 25, 2020. http://www.theses.fr/2019LYSE1241.

MLA Handbook (7th Edition):

Schmutz, Amandine. “Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport : Contributions to the analysis of multivariate functional data, application to the study of the sport horse's locomotion.” 2019. Web. 25 Feb 2020.

Vancouver:

Schmutz A. Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport : Contributions to the analysis of multivariate functional data, application to the study of the sport horse's locomotion. [Internet] [Doctoral dissertation]. Lyon; 2019. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2019LYSE1241.

Council of Science Editors:

Schmutz A. Contributions à l'analyse de données fonctionnelles multivariées, application à l'étude de la locomotion du cheval de sport : Contributions to the analysis of multivariate functional data, application to the study of the sport horse's locomotion. [Doctoral Dissertation]. Lyon; 2019. Available from: http://www.theses.fr/2019LYSE1241

16. Sublemontier, Jacques-Henri. Classification non supervisée : de la multiplicité des données à la multiplicité des analyses : Clustering : from multiple data to multiple analysis.

Degree: Docteur es, Informatique, 2012, Université d'Orléans

La classification automatique non supervisée est un problème majeur, aux frontières de multiples communautés issues de l’Intelligence Artificielle, de l’Analyse de Données et des Sciences… (more)

Subjects/Keywords: Intelligence Artificielle; Apprentissage automatique; Classification non supervisée; Données multi-vues; Consensus de partitions; Co-Apprentissage; Recherche d’alternatives; Artificial Intelligence; Machine Learning; Clustering; Multi-view data; Clustering ensemble; Co-Training; Alternative clustering

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

APA (6th Edition):

Sublemontier, J. (2012). Classification non supervisée : de la multiplicité des données à la multiplicité des analyses : Clustering : from multiple data to multiple analysis. (Doctoral Dissertation). Université d'Orléans. Retrieved from http://www.theses.fr/2012ORLE2064

Chicago Manual of Style (16th Edition):

Sublemontier, Jacques-Henri. “Classification non supervisée : de la multiplicité des données à la multiplicité des analyses : Clustering : from multiple data to multiple analysis.” 2012. Doctoral Dissertation, Université d'Orléans. Accessed February 25, 2020. http://www.theses.fr/2012ORLE2064.

MLA Handbook (7th Edition):

Sublemontier, Jacques-Henri. “Classification non supervisée : de la multiplicité des données à la multiplicité des analyses : Clustering : from multiple data to multiple analysis.” 2012. Web. 25 Feb 2020.

Vancouver:

Sublemontier J. Classification non supervisée : de la multiplicité des données à la multiplicité des analyses : Clustering : from multiple data to multiple analysis. [Internet] [Doctoral dissertation]. Université d'Orléans; 2012. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2012ORLE2064.

Council of Science Editors:

Sublemontier J. Classification non supervisée : de la multiplicité des données à la multiplicité des analyses : Clustering : from multiple data to multiple analysis. [Doctoral Dissertation]. Université d'Orléans; 2012. Available from: http://www.theses.fr/2012ORLE2064

17. Andhale, Pankaj. Semi-supervised heterogeneous evolutionary co-clustering.

Degree: Computer Science (GCCIS), 2012, Rochester Institute of Technology

 One of the challenges of the machine learning problem is the absence of sufficient number of labeled instances or training instances. At the same time… (more)

Subjects/Keywords: Clustering; Co-clustering; Data mining; Evolutionary; Hetrogeneous data; Semi-supervised

…x5D;. Only one dimension of data is used in clustering. Co-clustering is a technique which… …the instance feature relationship is used. The co-clustering approaches are either graph… …Copartitioning (CBGC)[11] and iso-perimetric co-clustering [27] have been… …used to perform co-clustering. Most of the real world data mining problems are heterogeneous… …the previous time step data. Nathan Green has performed the co-clustering on the evolving… 

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APA (6th Edition):

Andhale, P. (2012). Semi-supervised heterogeneous evolutionary co-clustering. (Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/4767

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):

Andhale, Pankaj. “Semi-supervised heterogeneous evolutionary co-clustering.” 2012. Thesis, Rochester Institute of Technology. Accessed February 25, 2020. https://scholarworks.rit.edu/theses/4767.

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

MLA Handbook (7th Edition):

Andhale, Pankaj. “Semi-supervised heterogeneous evolutionary co-clustering.” 2012. Web. 25 Feb 2020.

Vancouver:

Andhale P. Semi-supervised heterogeneous evolutionary co-clustering. [Internet] [Thesis]. Rochester Institute of Technology; 2012. [cited 2020 Feb 25]. Available from: https://scholarworks.rit.edu/theses/4767.

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

Council of Science Editors:

Andhale P. Semi-supervised heterogeneous evolutionary co-clustering. [Thesis]. Rochester Institute of Technology; 2012. Available from: https://scholarworks.rit.edu/theses/4767

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


Queensland University of Technology

18. Hou, Jun. Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering.

Degree: 2014, Queensland University of Technology

 This project is a step forward in the study of text mining where enhanced text representation with semantic information plays a significant role. It develops… (more)

Subjects/Keywords: Text Mining; Semantic Annotation; Entity-oriented Retrieval; Semantic Relation Identification; Clustering; Cluster Ensemble Learning; High-Order Co-Clustering; Multiple Subspace Learning; Concept-based Retrieval; Open Information Extraction

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APA (6th Edition):

Hou, J. (2014). Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/79206/

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):

Hou, Jun. “Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering.” 2014. Thesis, Queensland University of Technology. Accessed February 25, 2020. https://eprints.qut.edu.au/79206/.

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

MLA Handbook (7th Edition):

Hou, Jun. “Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering.” 2014. Web. 25 Feb 2020.

Vancouver:

Hou J. Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering. [Internet] [Thesis]. Queensland University of Technology; 2014. [cited 2020 Feb 25]. Available from: https://eprints.qut.edu.au/79206/.

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

Council of Science Editors:

Hou J. Text mining with semantic annotation : using enriched text representation for entity-oriented retrieval, semantic relation identification and text clustering. [Thesis]. Queensland University of Technology; 2014. Available from: https://eprints.qut.edu.au/79206/

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


Queensland University of Technology

19. Mohd Yusoh, Zeratul Izzah. Composite SaaS resource management in cloud computing using evolutionary computation.

Degree: 2013, Queensland University of Technology

 Cloud computing is an emerging computing paradigm in which IT resources are provided over the Internet as a service to users. One such service offered… (more)

Subjects/Keywords: software as a service; cloud computing; evolutionary computation; genetic algorithm; cooperative co-evolutionary algorithm; grouping genetic algorithm; optimisation; placement; clustering; scalability

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

APA (6th Edition):

Mohd Yusoh, Z. I. (2013). Composite SaaS resource management in cloud computing using evolutionary computation. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/63280/

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):

Mohd Yusoh, Zeratul Izzah. “Composite SaaS resource management in cloud computing using evolutionary computation.” 2013. Thesis, Queensland University of Technology. Accessed February 25, 2020. https://eprints.qut.edu.au/63280/.

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

MLA Handbook (7th Edition):

Mohd Yusoh, Zeratul Izzah. “Composite SaaS resource management in cloud computing using evolutionary computation.” 2013. Web. 25 Feb 2020.

Vancouver:

Mohd Yusoh ZI. Composite SaaS resource management in cloud computing using evolutionary computation. [Internet] [Thesis]. Queensland University of Technology; 2013. [cited 2020 Feb 25]. Available from: https://eprints.qut.edu.au/63280/.

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

Council of Science Editors:

Mohd Yusoh ZI. Composite SaaS resource management in cloud computing using evolutionary computation. [Thesis]. Queensland University of Technology; 2013. Available from: https://eprints.qut.edu.au/63280/

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


University of Wolverhampton

20. Kenekayoro, Patrick. Collaboration between UK Universities: A machine-learning based webometric analysis.

Degree: 2014, University of Wolverhampton

 Collaboration is essential for some types of research, which is why some agencies include collaboration among the requirements for funding research projects. Studying collaborative relationships… (more)

Subjects/Keywords: collaboration; university; machine learning; supervised learning; unsupervised learning; webometrics; link analysis; co-word analysis; classification; clustering

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APA (6th Edition):

Kenekayoro, P. (2014). Collaboration between UK Universities: A machine-learning based webometric analysis. (Thesis). University of Wolverhampton. Retrieved from http://hdl.handle.net/2436/338261

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):

Kenekayoro, Patrick. “Collaboration between UK Universities: A machine-learning based webometric analysis.” 2014. Thesis, University of Wolverhampton. Accessed February 25, 2020. http://hdl.handle.net/2436/338261.

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

MLA Handbook (7th Edition):

Kenekayoro, Patrick. “Collaboration between UK Universities: A machine-learning based webometric analysis.” 2014. Web. 25 Feb 2020.

Vancouver:

Kenekayoro P. Collaboration between UK Universities: A machine-learning based webometric analysis. [Internet] [Thesis]. University of Wolverhampton; 2014. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2436/338261.

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

Council of Science Editors:

Kenekayoro P. Collaboration between UK Universities: A machine-learning based webometric analysis. [Thesis]. University of Wolverhampton; 2014. Available from: http://hdl.handle.net/2436/338261

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

21. Brunet, Anne-Claire. Développement d'outils statistiques pour l'analyse de données transcriptomiques par les réseaux de co-expression de gènes : A systemic approach to statistical analysis to transcriptomic data through co-expression network analysis.

Degree: Docteur es, Mathématiques appliquées, 2016, Université Toulouse III – Paul Sabatier

 Les nouvelles biotechnologies offrent aujourd'hui la possibilité de récolter une très grande variété et quantité de données biologiques (génomique, protéomique, métagénomique...), ouvrant ainsi de nouvelles… (more)

Subjects/Keywords: Données transcriptomiques; Réseaux de gènes; Transcriptomic data; Co-expression network; Variable selection; Dimensionality reduction; Penalized regression; Network clustering; Machine learning

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APA (6th Edition):

Brunet, A. (2016). Développement d'outils statistiques pour l'analyse de données transcriptomiques par les réseaux de co-expression de gènes : A systemic approach to statistical analysis to transcriptomic data through co-expression network analysis. (Doctoral Dissertation). Université Toulouse III – Paul Sabatier. Retrieved from http://www.theses.fr/2016TOU30373

Chicago Manual of Style (16th Edition):

Brunet, Anne-Claire. “Développement d'outils statistiques pour l'analyse de données transcriptomiques par les réseaux de co-expression de gènes : A systemic approach to statistical analysis to transcriptomic data through co-expression network analysis.” 2016. Doctoral Dissertation, Université Toulouse III – Paul Sabatier. Accessed February 25, 2020. http://www.theses.fr/2016TOU30373.

MLA Handbook (7th Edition):

Brunet, Anne-Claire. “Développement d'outils statistiques pour l'analyse de données transcriptomiques par les réseaux de co-expression de gènes : A systemic approach to statistical analysis to transcriptomic data through co-expression network analysis.” 2016. Web. 25 Feb 2020.

Vancouver:

Brunet A. Développement d'outils statistiques pour l'analyse de données transcriptomiques par les réseaux de co-expression de gènes : A systemic approach to statistical analysis to transcriptomic data through co-expression network analysis. [Internet] [Doctoral dissertation]. Université Toulouse III – Paul Sabatier; 2016. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2016TOU30373.

Council of Science Editors:

Brunet A. Développement d'outils statistiques pour l'analyse de données transcriptomiques par les réseaux de co-expression de gènes : A systemic approach to statistical analysis to transcriptomic data through co-expression network analysis. [Doctoral Dissertation]. Université Toulouse III – Paul Sabatier; 2016. Available from: http://www.theses.fr/2016TOU30373

22. WU WEI. Positional clustering and co-expression analysis of neighboring genes in the zebrafish genome.

Degree: 2006, National University of Singapore

Subjects/Keywords: Positional Clustering; Co-expression; Zebrafish; Microarray; Neighboring Gene

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APA (6th Edition):

WEI, W. (2006). Positional clustering and co-expression analysis of neighboring genes in the zebrafish genome. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/15343

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, WU. “Positional clustering and co-expression analysis of neighboring genes in the zebrafish genome.” 2006. Thesis, National University of Singapore. Accessed February 25, 2020. http://scholarbank.nus.edu.sg/handle/10635/15343.

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

MLA Handbook (7th Edition):

WEI, WU. “Positional clustering and co-expression analysis of neighboring genes in the zebrafish genome.” 2006. Web. 25 Feb 2020.

Vancouver:

WEI W. Positional clustering and co-expression analysis of neighboring genes in the zebrafish genome. [Internet] [Thesis]. National University of Singapore; 2006. [cited 2020 Feb 25]. Available from: http://scholarbank.nus.edu.sg/handle/10635/15343.

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

Council of Science Editors:

WEI W. Positional clustering and co-expression analysis of neighboring genes in the zebrafish genome. [Thesis]. National University of Singapore; 2006. Available from: http://scholarbank.nus.edu.sg/handle/10635/15343

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

23. Makkhongkaew, Raywat. Semi-supervised co-selection : instances and features : application to diagnosis of dry port by rail : Co-selection instances-variables en mode semi-supervisé : application au diagnostic de transport ferroviaire.

Degree: Docteur es, Informatique, 2016, Lyon

Depuis la prolifération des bases de données partiellement étiquetées, l'apprentissage automatique a connu un développement important dans le mode semi-supervisé. Cette tendance est due à… (more)

Subjects/Keywords: Sélection d'instances; Sélection de variables; Co-selection; Apprentissage semi-supervisé; Classification sous contraintes; Instance selection; Feature selecion; Co-selection; Semi-supervised learning; Constrained clustering; 006.3

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APA (6th Edition):

Makkhongkaew, R. (2016). Semi-supervised co-selection : instances and features : application to diagnosis of dry port by rail : Co-selection instances-variables en mode semi-supervisé : application au diagnostic de transport ferroviaire. (Doctoral Dissertation). Lyon. Retrieved from http://www.theses.fr/2016LYSE1341

Chicago Manual of Style (16th Edition):

Makkhongkaew, Raywat. “Semi-supervised co-selection : instances and features : application to diagnosis of dry port by rail : Co-selection instances-variables en mode semi-supervisé : application au diagnostic de transport ferroviaire.” 2016. Doctoral Dissertation, Lyon. Accessed February 25, 2020. http://www.theses.fr/2016LYSE1341.

MLA Handbook (7th Edition):

Makkhongkaew, Raywat. “Semi-supervised co-selection : instances and features : application to diagnosis of dry port by rail : Co-selection instances-variables en mode semi-supervisé : application au diagnostic de transport ferroviaire.” 2016. Web. 25 Feb 2020.

Vancouver:

Makkhongkaew R. Semi-supervised co-selection : instances and features : application to diagnosis of dry port by rail : Co-selection instances-variables en mode semi-supervisé : application au diagnostic de transport ferroviaire. [Internet] [Doctoral dissertation]. Lyon; 2016. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2016LYSE1341.

Council of Science Editors:

Makkhongkaew R. Semi-supervised co-selection : instances and features : application to diagnosis of dry port by rail : Co-selection instances-variables en mode semi-supervisé : application au diagnostic de transport ferroviaire. [Doctoral Dissertation]. Lyon; 2016. Available from: http://www.theses.fr/2016LYSE1341

24. Ailem, Melissa. Sparsity-sensitive diagonal co-clustering algorithms for the effective handling of text data : Algorithmes d'optimisation du processus d'allocation de ressources pour l'infrastructure en tant que service en informatique en nuage.

Degree: Docteur es, Science de données, 2016, Sorbonne Paris Cité

 Dans le contexte actuel, il y a un besoin évident de techniques de fouille de textes pour analyser l'énorme quantité de documents textuelles non structurées… (more)

Subjects/Keywords: Classification croisée; Modularité de graphes; Modèles de mélanges; Fouille de textes; Matrices creuses; Données textuelles; Matrices document-terme; Co-clustering; Graph modularity; Mixture models; Text mining; Sparse data; Text data; Document-term matrices; Model-based co-clustering; 004.678

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APA (6th Edition):

Ailem, M. (2016). Sparsity-sensitive diagonal co-clustering algorithms for the effective handling of text data : Algorithmes d'optimisation du processus d'allocation de ressources pour l'infrastructure en tant que service en informatique en nuage. (Doctoral Dissertation). Sorbonne Paris Cité. Retrieved from http://www.theses.fr/2016USPCB087

Chicago Manual of Style (16th Edition):

Ailem, Melissa. “Sparsity-sensitive diagonal co-clustering algorithms for the effective handling of text data : Algorithmes d'optimisation du processus d'allocation de ressources pour l'infrastructure en tant que service en informatique en nuage.” 2016. Doctoral Dissertation, Sorbonne Paris Cité. Accessed February 25, 2020. http://www.theses.fr/2016USPCB087.

MLA Handbook (7th Edition):

Ailem, Melissa. “Sparsity-sensitive diagonal co-clustering algorithms for the effective handling of text data : Algorithmes d'optimisation du processus d'allocation de ressources pour l'infrastructure en tant que service en informatique en nuage.” 2016. Web. 25 Feb 2020.

Vancouver:

Ailem M. Sparsity-sensitive diagonal co-clustering algorithms for the effective handling of text data : Algorithmes d'optimisation du processus d'allocation de ressources pour l'infrastructure en tant que service en informatique en nuage. [Internet] [Doctoral dissertation]. Sorbonne Paris Cité; 2016. [cited 2020 Feb 25]. Available from: http://www.theses.fr/2016USPCB087.

Council of Science Editors:

Ailem M. Sparsity-sensitive diagonal co-clustering algorithms for the effective handling of text data : Algorithmes d'optimisation du processus d'allocation de ressources pour l'infrastructure en tant que service en informatique en nuage. [Doctoral Dissertation]. Sorbonne Paris Cité; 2016. Available from: http://www.theses.fr/2016USPCB087

25. GUO JIAMING. CONTENT EXTRACTION BASED ON VIDEO CO-SEGMENTATION.

Degree: 2014, National University of Singapore

Subjects/Keywords: content extraction; video segmentation; video action co-segmentation; video object co-segmentation; co-saliency; constraint clustering

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APA (6th Edition):

JIAMING, G. (2014). CONTENT EXTRACTION BASED ON VIDEO CO-SEGMENTATION. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/121113

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):

JIAMING, GUO. “CONTENT EXTRACTION BASED ON VIDEO CO-SEGMENTATION.” 2014. Thesis, National University of Singapore. Accessed February 25, 2020. http://scholarbank.nus.edu.sg/handle/10635/121113.

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

MLA Handbook (7th Edition):

JIAMING, GUO. “CONTENT EXTRACTION BASED ON VIDEO CO-SEGMENTATION.” 2014. Web. 25 Feb 2020.

Vancouver:

JIAMING G. CONTENT EXTRACTION BASED ON VIDEO CO-SEGMENTATION. [Internet] [Thesis]. National University of Singapore; 2014. [cited 2020 Feb 25]. Available from: http://scholarbank.nus.edu.sg/handle/10635/121113.

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

Council of Science Editors:

JIAMING G. CONTENT EXTRACTION BASED ON VIDEO CO-SEGMENTATION. [Thesis]. National University of Singapore; 2014. Available from: http://scholarbank.nus.edu.sg/handle/10635/121113

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


University of Florida

26. Almutairi, Abdullah. Efficient Algorithms for Learning Correlations in Large-Scale Wireless Data.

Degree: PhD, Computer Engineering - Computer and Information Science and Engineering, 2012, University of Florida

 Wireless mobile networks are experiencing tremendous growth and increased presence. Data collected for mobile users can be effectively used to design more effective networks as… (more)

Subjects/Keywords: Approximation; Behavior modeling; Correlations; Data visualization; Datasets; Information behavior; Learning; Mining; Perceptual localization; Social media; co-clustering  – correlations  – data  – mixture  – models  – networks  – wireless

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

APA (6th Edition):

Almutairi, A. (2012). Efficient Algorithms for Learning Correlations in Large-Scale Wireless Data. (Doctoral Dissertation). University of Florida. Retrieved from http://ufdc.ufl.edu/UFE0044610

Chicago Manual of Style (16th Edition):

Almutairi, Abdullah. “Efficient Algorithms for Learning Correlations in Large-Scale Wireless Data.” 2012. Doctoral Dissertation, University of Florida. Accessed February 25, 2020. http://ufdc.ufl.edu/UFE0044610.

MLA Handbook (7th Edition):

Almutairi, Abdullah. “Efficient Algorithms for Learning Correlations in Large-Scale Wireless Data.” 2012. Web. 25 Feb 2020.

Vancouver:

Almutairi A. Efficient Algorithms for Learning Correlations in Large-Scale Wireless Data. [Internet] [Doctoral dissertation]. University of Florida; 2012. [cited 2020 Feb 25]. Available from: http://ufdc.ufl.edu/UFE0044610.

Council of Science Editors:

Almutairi A. Efficient Algorithms for Learning Correlations in Large-Scale Wireless Data. [Doctoral Dissertation]. University of Florida; 2012. Available from: http://ufdc.ufl.edu/UFE0044610


Carnegie Mellon University

27. Jang, Jiyong. Scaling Software Security Analysis to Millions of Malicious Programs and Billions of Lines of Code.

Degree: 2013, Carnegie Mellon University

 Software security is a big data problem. The volume of new software artifacts created far outpaces the current capacity of software analysis. This gap has… (more)

Subjects/Keywords: Malware; Triage; Feature Hashing; Co-clustering; Hadoop; Unpatched Code Clone; Bloom Filter; Lineage; Binary Analysis; Code Reuse; Big Data; Electrical and Computer Engineering

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APA (6th Edition):

Jang, J. (2013). Scaling Software Security Analysis to Millions of Malicious Programs and Billions of Lines of Code. (Thesis). Carnegie Mellon University. Retrieved from http://repository.cmu.edu/dissertations/306

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):

Jang, Jiyong. “Scaling Software Security Analysis to Millions of Malicious Programs and Billions of Lines of Code.” 2013. Thesis, Carnegie Mellon University. Accessed February 25, 2020. http://repository.cmu.edu/dissertations/306.

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

MLA Handbook (7th Edition):

Jang, Jiyong. “Scaling Software Security Analysis to Millions of Malicious Programs and Billions of Lines of Code.” 2013. Web. 25 Feb 2020.

Vancouver:

Jang J. Scaling Software Security Analysis to Millions of Malicious Programs and Billions of Lines of Code. [Internet] [Thesis]. Carnegie Mellon University; 2013. [cited 2020 Feb 25]. Available from: http://repository.cmu.edu/dissertations/306.

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

Council of Science Editors:

Jang J. Scaling Software Security Analysis to Millions of Malicious Programs and Billions of Lines of Code. [Thesis]. Carnegie Mellon University; 2013. Available from: http://repository.cmu.edu/dissertations/306

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


University of Illinois – Urbana-Champaign

28. Yang, Huiguang. From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming.

Degree: PhD, Electrical & Computer Engr, 2016, University of Illinois – Urbana-Champaign

 This dissertation aims to explore the ideas and frameworks for solving the discrete optimization problem in computer vision. Much of the work is inspired by… (more)

Subjects/Keywords: image co-segmentation; graphical model; energy minimization; integer programming; statistical physics; discrete optimization; Mixed-Integer Quadratic Programming (MIQP); clustering; local topology consistency check; sparse optimization

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

APA (6th Edition):

Yang, H. (2016). From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/95489

Chicago Manual of Style (16th Edition):

Yang, Huiguang. “From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming.” 2016. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed February 25, 2020. http://hdl.handle.net/2142/95489.

MLA Handbook (7th Edition):

Yang, Huiguang. “From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming.” 2016. Web. 25 Feb 2020.

Vancouver:

Yang H. From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Feb 25]. Available from: http://hdl.handle.net/2142/95489.

Council of Science Editors:

Yang H. From image co-segmentation to discrete optimization in computer vision - the exploration on graphical model, statistical physics, energy minimization, and integer programming. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/95489

29. Garcia Lopes Maia Rodrigues, J. Computational Structural Biology of Macromolecular Interactions.

Degree: 2014, University Utrecht

 The living cell is a formidable entity kept intact and functioning by a network of interactions carried out by protein molecules. As such, understanding this… (more)

Subjects/Keywords: protein docking; HADDOCK; integrative modeling; co-evolution; clustering; protein interactions

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

APA (6th Edition):

Garcia Lopes Maia Rodrigues, J. (2014). Computational Structural Biology of Macromolecular Interactions. (Doctoral Dissertation). University Utrecht. Retrieved from http://dspace.library.uu.nl/handle/1874/307246 ; URN:NBN:NL:UI:10-1874-307246 ; urn:isbn:978-90-5335-983-9 ; URN:NBN:NL:UI:10-1874-307246 ; http://dspace.library.uu.nl/handle/1874/307246

Chicago Manual of Style (16th Edition):

Garcia Lopes Maia Rodrigues, J. “Computational Structural Biology of Macromolecular Interactions.” 2014. Doctoral Dissertation, University Utrecht. Accessed February 25, 2020. http://dspace.library.uu.nl/handle/1874/307246 ; URN:NBN:NL:UI:10-1874-307246 ; urn:isbn:978-90-5335-983-9 ; URN:NBN:NL:UI:10-1874-307246 ; http://dspace.library.uu.nl/handle/1874/307246.

MLA Handbook (7th Edition):

Garcia Lopes Maia Rodrigues, J. “Computational Structural Biology of Macromolecular Interactions.” 2014. Web. 25 Feb 2020.

Vancouver:

Garcia Lopes Maia Rodrigues J. Computational Structural Biology of Macromolecular Interactions. [Internet] [Doctoral dissertation]. University Utrecht; 2014. [cited 2020 Feb 25]. Available from: http://dspace.library.uu.nl/handle/1874/307246 ; URN:NBN:NL:UI:10-1874-307246 ; urn:isbn:978-90-5335-983-9 ; URN:NBN:NL:UI:10-1874-307246 ; http://dspace.library.uu.nl/handle/1874/307246.

Council of Science Editors:

Garcia Lopes Maia Rodrigues J. Computational Structural Biology of Macromolecular Interactions. [Doctoral Dissertation]. University Utrecht; 2014. Available from: http://dspace.library.uu.nl/handle/1874/307246 ; URN:NBN:NL:UI:10-1874-307246 ; urn:isbn:978-90-5335-983-9 ; URN:NBN:NL:UI:10-1874-307246 ; http://dspace.library.uu.nl/handle/1874/307246

30. XU MINGYING. Spatial Proximity, Co-authorships and Localized Knowledge Spillovers: A Case Study of Biopolis in Singapore.

Degree: 2013, National University of Singapore

Subjects/Keywords: spatial proximity; co-authorship; knowledge spillover; Biopolis; agglomeration; clustering effect

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

APA (6th Edition):

MINGYING, X. (2013). Spatial Proximity, Co-authorships and Localized Knowledge Spillovers: A Case Study of Biopolis in Singapore. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/37909

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):

MINGYING, XU. “Spatial Proximity, Co-authorships and Localized Knowledge Spillovers: A Case Study of Biopolis in Singapore.” 2013. Thesis, National University of Singapore. Accessed February 25, 2020. http://scholarbank.nus.edu.sg/handle/10635/37909.

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

MLA Handbook (7th Edition):

MINGYING, XU. “Spatial Proximity, Co-authorships and Localized Knowledge Spillovers: A Case Study of Biopolis in Singapore.” 2013. Web. 25 Feb 2020.

Vancouver:

MINGYING X. Spatial Proximity, Co-authorships and Localized Knowledge Spillovers: A Case Study of Biopolis in Singapore. [Internet] [Thesis]. National University of Singapore; 2013. [cited 2020 Feb 25]. Available from: http://scholarbank.nus.edu.sg/handle/10635/37909.

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

Council of Science Editors:

MINGYING X. Spatial Proximity, Co-authorships and Localized Knowledge Spillovers: A Case Study of Biopolis in Singapore. [Thesis]. National University of Singapore; 2013. Available from: http://scholarbank.nus.edu.sg/handle/10635/37909

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

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