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You searched for subject:(unsupervised clustering). Showing records 1 – 30 of 145 total matches.

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Penn State University

1. Jha, Manjari. Probabilistic Techniques for Metagenomic Clustering and Intrusion Detection.

Degree: 2018, Penn State University

 This thesis focuses on developing probabilistic models for the analysis of diverse datasets using unsupervised clustering techniques. Primarily, we focus on two main fields: the… (more)

Subjects/Keywords: metagenomics; intrusion detection; clustering; unsupervised

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APA (6th Edition):

Jha, M. (2018). Probabilistic Techniques for Metagenomic Clustering and Intrusion Detection. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/15543mom5590

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):

Jha, Manjari. “Probabilistic Techniques for Metagenomic Clustering and Intrusion Detection.” 2018. Thesis, Penn State University. Accessed September 28, 2020. https://submit-etda.libraries.psu.edu/catalog/15543mom5590.

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

MLA Handbook (7th Edition):

Jha, Manjari. “Probabilistic Techniques for Metagenomic Clustering and Intrusion Detection.” 2018. Web. 28 Sep 2020.

Vancouver:

Jha M. Probabilistic Techniques for Metagenomic Clustering and Intrusion Detection. [Internet] [Thesis]. Penn State University; 2018. [cited 2020 Sep 28]. Available from: https://submit-etda.libraries.psu.edu/catalog/15543mom5590.

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

Council of Science Editors:

Jha M. Probabilistic Techniques for Metagenomic Clustering and Intrusion Detection. [Thesis]. Penn State University; 2018. Available from: https://submit-etda.libraries.psu.edu/catalog/15543mom5590

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


George Mason University

2. Wang, Pu. Nonparametric Bayesian Models for Unsupervised Learning .

Degree: 2011, George Mason University

Unsupervised learning is an important topic in machine learning. In particular, clustering is an unsupervised learning problem that arises in a variety of applications for… (more)

Subjects/Keywords: Unsupervised Learning; Clustering; Bayesian Nonparametrics; Clustering Ensembles

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APA (6th Edition):

Wang, P. (2011). Nonparametric Bayesian Models for Unsupervised Learning . (Thesis). George Mason University. Retrieved from http://hdl.handle.net/1920/6360

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):

Wang, Pu. “Nonparametric Bayesian Models for Unsupervised Learning .” 2011. Thesis, George Mason University. Accessed September 28, 2020. http://hdl.handle.net/1920/6360.

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

MLA Handbook (7th Edition):

Wang, Pu. “Nonparametric Bayesian Models for Unsupervised Learning .” 2011. Web. 28 Sep 2020.

Vancouver:

Wang P. Nonparametric Bayesian Models for Unsupervised Learning . [Internet] [Thesis]. George Mason University; 2011. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1920/6360.

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

Council of Science Editors:

Wang P. Nonparametric Bayesian Models for Unsupervised Learning . [Thesis]. George Mason University; 2011. Available from: http://hdl.handle.net/1920/6360

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


Universidade Nova

3. Madsen, Jacob Hastrup. Outlier detection for improved clustering : empirical research for unsupervised data mining.

Degree: 2018, Universidade Nova

 Many clustering algorithms are sensitive to noise disturbing the results when trying to identify and characterize clusters in data. Due to the multidimensional nature of… (more)

Subjects/Keywords: Outlier Detection; Unsupervised Learning; Clustering; Data Mining

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APA (6th Edition):

Madsen, J. H. (2018). Outlier detection for improved clustering : empirical research for unsupervised data mining. (Thesis). Universidade Nova. Retrieved from https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464

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):

Madsen, Jacob Hastrup. “Outlier detection for improved clustering : empirical research for unsupervised data mining.” 2018. Thesis, Universidade Nova. Accessed September 28, 2020. https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464.

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

MLA Handbook (7th Edition):

Madsen, Jacob Hastrup. “Outlier detection for improved clustering : empirical research for unsupervised data mining.” 2018. Web. 28 Sep 2020.

Vancouver:

Madsen JH. Outlier detection for improved clustering : empirical research for unsupervised data mining. [Internet] [Thesis]. Universidade Nova; 2018. [cited 2020 Sep 28]. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464.

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

Council of Science Editors:

Madsen JH. Outlier detection for improved clustering : empirical research for unsupervised data mining. [Thesis]. Universidade Nova; 2018. Available from: https://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/34464

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


University of Connecticut

4. Yankee, Tara N. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.

Degree: M. Eng., Biomedical Engineering, 2017, University of Connecticut

  The ability to collect and store large amounts of data is transforming data-driven discovery; recent technological advances in biology allow systematic data production and… (more)

Subjects/Keywords: clustering; ensemble learning; feature selection; unsupervised learning

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APA (6th Edition):

Yankee, T. N. (2017). Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. (Masters Thesis). University of Connecticut. Retrieved from https://opencommons.uconn.edu/gs_theses/1123

Chicago Manual of Style (16th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Masters Thesis, University of Connecticut. Accessed September 28, 2020. https://opencommons.uconn.edu/gs_theses/1123.

MLA Handbook (7th Edition):

Yankee, Tara N. “Rank Aggregation of Feature Scoring Methods for Unsupervised Learning.” 2017. Web. 28 Sep 2020.

Vancouver:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Internet] [Masters thesis]. University of Connecticut; 2017. [cited 2020 Sep 28]. Available from: https://opencommons.uconn.edu/gs_theses/1123.

Council of Science Editors:

Yankee TN. Rank Aggregation of Feature Scoring Methods for Unsupervised Learning. [Masters Thesis]. University of Connecticut; 2017. Available from: https://opencommons.uconn.edu/gs_theses/1123


University of Toronto

5. Miasnikof, Pierre. Subgraph Density and Graph Clustering.

Degree: PhD, 2019, University of Toronto

 Graph clustering, also often referred to as network community detection, is an unsupervised learning task. It is the process of grouping vertices into sets of… (more)

Subjects/Keywords: Data Science; Graph Clustering; Unsupervised Learning; 0463

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APA (6th Edition):

Miasnikof, P. (2019). Subgraph Density and Graph Clustering. (Doctoral Dissertation). University of Toronto. Retrieved from http://hdl.handle.net/1807/97609

Chicago Manual of Style (16th Edition):

Miasnikof, Pierre. “Subgraph Density and Graph Clustering.” 2019. Doctoral Dissertation, University of Toronto. Accessed September 28, 2020. http://hdl.handle.net/1807/97609.

MLA Handbook (7th Edition):

Miasnikof, Pierre. “Subgraph Density and Graph Clustering.” 2019. Web. 28 Sep 2020.

Vancouver:

Miasnikof P. Subgraph Density and Graph Clustering. [Internet] [Doctoral dissertation]. University of Toronto; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1807/97609.

Council of Science Editors:

Miasnikof P. Subgraph Density and Graph Clustering. [Doctoral Dissertation]. University of Toronto; 2019. Available from: http://hdl.handle.net/1807/97609


University of Minnesota

6. Traganitis, Panagiotis. Scalable and Ensemble Learning for Big Data.

Degree: PhD, Electrical/Computer Engineering, 2019, University of Minnesota

 The turn of the decade has trademarked society and computing research with a ``data deluge.'' As the number of smart, highly accurate and Internet-capable devices… (more)

Subjects/Keywords: Big Data; clustering; Ensemble; learning; subspace; unsupervised

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APA (6th Edition):

Traganitis, P. (2019). Scalable and Ensemble Learning for Big Data. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206358

Chicago Manual of Style (16th Edition):

Traganitis, Panagiotis. “Scalable and Ensemble Learning for Big Data.” 2019. Doctoral Dissertation, University of Minnesota. Accessed September 28, 2020. http://hdl.handle.net/11299/206358.

MLA Handbook (7th Edition):

Traganitis, Panagiotis. “Scalable and Ensemble Learning for Big Data.” 2019. Web. 28 Sep 2020.

Vancouver:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/11299/206358.

Council of Science Editors:

Traganitis P. Scalable and Ensemble Learning for Big Data. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206358


Texas Tech University

7. Hill, Jason E. Application of Information Theoretic Unsupervised Learning to Medical Image Analysis.

Degree: 2013, Texas Tech University

 Automated segmentation of medical images is a challenging problem. The number of segments in a medical image may be unknown a priori, due to the… (more)

Subjects/Keywords: Unsupervised learning; Medical images; Spectral clustering.

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APA (6th Edition):

Hill, J. E. (2013). Application of Information Theoretic Unsupervised Learning to Medical Image Analysis. (Thesis). Texas Tech University. Retrieved from http://hdl.handle.net/2346/48865

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):

Hill, Jason E. “Application of Information Theoretic Unsupervised Learning to Medical Image Analysis.” 2013. Thesis, Texas Tech University. Accessed September 28, 2020. http://hdl.handle.net/2346/48865.

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

MLA Handbook (7th Edition):

Hill, Jason E. “Application of Information Theoretic Unsupervised Learning to Medical Image Analysis.” 2013. Web. 28 Sep 2020.

Vancouver:

Hill JE. Application of Information Theoretic Unsupervised Learning to Medical Image Analysis. [Internet] [Thesis]. Texas Tech University; 2013. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2346/48865.

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

Council of Science Editors:

Hill JE. Application of Information Theoretic Unsupervised Learning to Medical Image Analysis. [Thesis]. Texas Tech University; 2013. Available from: http://hdl.handle.net/2346/48865

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


Georgia Tech

8. Liang, Yingyu. Modern aspects of unsupervised learning.

Degree: PhD, Computer Science, 2014, Georgia Tech

Unsupervised learning has become more and more important due to the recent explosion of data. Clustering, a key topic in unsupervised learning, is a well-studied… (more)

Subjects/Keywords: Unsupervised learning; Clustering; Perturbation resilience; Distributed clustering; Community detection

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APA (6th Edition):

Liang, Y. (2014). Modern aspects of unsupervised learning. (Doctoral Dissertation). Georgia Tech. Retrieved from http://hdl.handle.net/1853/52282

Chicago Manual of Style (16th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Doctoral Dissertation, Georgia Tech. Accessed September 28, 2020. http://hdl.handle.net/1853/52282.

MLA Handbook (7th Edition):

Liang, Yingyu. “Modern aspects of unsupervised learning.” 2014. Web. 28 Sep 2020.

Vancouver:

Liang Y. Modern aspects of unsupervised learning. [Internet] [Doctoral dissertation]. Georgia Tech; 2014. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1853/52282.

Council of Science Editors:

Liang Y. Modern aspects of unsupervised learning. [Doctoral Dissertation]. Georgia Tech; 2014. Available from: http://hdl.handle.net/1853/52282

9. Ghesmoune, Mohammed. Apprentissage non supervisé de flux de données massives : application aux Big Data d'assurance : Unsupervided learning of massive data streams : application to Big Data in insurance.

Degree: Docteur es, Informatique, 2016, Sorbonne Paris Cité

Le travail de recherche exposé dans cette thèse concerne le développement d'approches à base de growing neural gas (GNG) pour le clustering de flux de… (more)

Subjects/Keywords: Apprentissage no supervisé; Clustering de flux de données; Clustering topologique; MapReduce; Unsupervised learning; Clustering of data streams; Topogical clustering

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

APA (6th Edition):

Ghesmoune, M. (2016). Apprentissage non supervisé de flux de données massives : application aux Big Data d'assurance : Unsupervided learning of massive data streams : application to Big Data in insurance. (Doctoral Dissertation). Sorbonne Paris Cité. Retrieved from http://www.theses.fr/2016USPCD061

Chicago Manual of Style (16th Edition):

Ghesmoune, Mohammed. “Apprentissage non supervisé de flux de données massives : application aux Big Data d'assurance : Unsupervided learning of massive data streams : application to Big Data in insurance.” 2016. Doctoral Dissertation, Sorbonne Paris Cité. Accessed September 28, 2020. http://www.theses.fr/2016USPCD061.

MLA Handbook (7th Edition):

Ghesmoune, Mohammed. “Apprentissage non supervisé de flux de données massives : application aux Big Data d'assurance : Unsupervided learning of massive data streams : application to Big Data in insurance.” 2016. Web. 28 Sep 2020.

Vancouver:

Ghesmoune M. Apprentissage non supervisé de flux de données massives : application aux Big Data d'assurance : Unsupervided learning of massive data streams : application to Big Data in insurance. [Internet] [Doctoral dissertation]. Sorbonne Paris Cité; 2016. [cited 2020 Sep 28]. Available from: http://www.theses.fr/2016USPCD061.

Council of Science Editors:

Ghesmoune M. Apprentissage non supervisé de flux de données massives : application aux Big Data d'assurance : Unsupervided learning of massive data streams : application to Big Data in insurance. [Doctoral Dissertation]. Sorbonne Paris Cité; 2016. Available from: http://www.theses.fr/2016USPCD061

10. Sublime, Jérémie. Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution : Contributions to collaborative clustering and its potential applications on very high resolution satellite images.

Degree: Docteur es, Informatique appliquée, 2016, Université Paris-Saclay (ComUE)

Cette thèse présente plusieurs algorithmes développés dans le cadre du projet ANR COCLICO et contient deux axes principaux :Le premier axe concerne l'introduction d'un algorithme… (more)

Subjects/Keywords: Apprentissage non-Supervisé; Clustering collaboratif; Segmentation d'image; Unsupervised learning; Collaborative clustering; Image segmentation; 003.3

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APA (6th Edition):

Sublime, J. (2016). Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution : Contributions to collaborative clustering and its potential applications on very high resolution satellite images. (Doctoral Dissertation). Université Paris-Saclay (ComUE). Retrieved from http://www.theses.fr/2016SACLA005

Chicago Manual of Style (16th Edition):

Sublime, Jérémie. “Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution : Contributions to collaborative clustering and its potential applications on very high resolution satellite images.” 2016. Doctoral Dissertation, Université Paris-Saclay (ComUE). Accessed September 28, 2020. http://www.theses.fr/2016SACLA005.

MLA Handbook (7th Edition):

Sublime, Jérémie. “Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution : Contributions to collaborative clustering and its potential applications on very high resolution satellite images.” 2016. Web. 28 Sep 2020.

Vancouver:

Sublime J. Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution : Contributions to collaborative clustering and its potential applications on very high resolution satellite images. [Internet] [Doctoral dissertation]. Université Paris-Saclay (ComUE); 2016. [cited 2020 Sep 28]. Available from: http://www.theses.fr/2016SACLA005.

Council of Science Editors:

Sublime J. Contributions au clustering collaboratif et à ses potentielles applications en imagerie à très haute résolution : Contributions to collaborative clustering and its potential applications on very high resolution satellite images. [Doctoral Dissertation]. Université Paris-Saclay (ComUE); 2016. Available from: http://www.theses.fr/2016SACLA005


University of Waterloo

11. Waraich, Saad Ahmed. Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering.

Degree: 2020, University of Waterloo

 Blood flow visualization is a challenging task in the presence of tissue motion. Unsuppressed tissue clutter produces flashing artefacts in ultrasound flow imaging which hampers… (more)

Subjects/Keywords: singular value decomposition; ultrasound imaging; unsupervised learning; clustering

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APA (6th Edition):

Waraich, S. A. (2020). Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/15544

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):

Waraich, Saad Ahmed. “Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering.” 2020. Thesis, University of Waterloo. Accessed September 28, 2020. http://hdl.handle.net/10012/15544.

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

MLA Handbook (7th Edition):

Waraich, Saad Ahmed. “Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering.” 2020. Web. 28 Sep 2020.

Vancouver:

Waraich SA. Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering. [Internet] [Thesis]. University of Waterloo; 2020. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10012/15544.

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

Council of Science Editors:

Waraich SA. Robust Eigen-Filter Design for Ultrasound Flow Imaging Using a Multivariate Clustering. [Thesis]. University of Waterloo; 2020. Available from: http://hdl.handle.net/10012/15544

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

12. ZHANG ZHENJIE. Local bounding technique and its applications to uncertain clustering.

Degree: 2010, National University of Singapore

Subjects/Keywords: Clustering; Unsupervised; Learning; Uncertainty

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APA (6th Edition):

ZHENJIE, Z. (2010). Local bounding technique and its applications to uncertain clustering. (Thesis). National University of Singapore. Retrieved from http://scholarbank.nus.edu.sg/handle/10635/18429

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):

ZHENJIE, ZHANG. “Local bounding technique and its applications to uncertain clustering.” 2010. Thesis, National University of Singapore. Accessed September 28, 2020. http://scholarbank.nus.edu.sg/handle/10635/18429.

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

MLA Handbook (7th Edition):

ZHENJIE, ZHANG. “Local bounding technique and its applications to uncertain clustering.” 2010. Web. 28 Sep 2020.

Vancouver:

ZHENJIE Z. Local bounding technique and its applications to uncertain clustering. [Internet] [Thesis]. National University of Singapore; 2010. [cited 2020 Sep 28]. Available from: http://scholarbank.nus.edu.sg/handle/10635/18429.

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

Council of Science Editors:

ZHENJIE Z. Local bounding technique and its applications to uncertain clustering. [Thesis]. National University of Singapore; 2010. Available from: http://scholarbank.nus.edu.sg/handle/10635/18429

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


Delft University of Technology

13. Ciulei, Victor (author). Cluster-based Analysis of Airline Adherence to Flight Plan: The European Case.

Degree: 2020, Delft University of Technology

Airlines plan the trajectory of their flights in advance. However, this plan is not always followed since, during the actual flight, aircraft deviate either horizontally… (more)

Subjects/Keywords: flight plan deviations; unsupervised Machine Learning; airline clustering; flight efficiency

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APA (6th Edition):

Ciulei, V. (. (2020). Cluster-based Analysis of Airline Adherence to Flight Plan: The European Case. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:3455449e-04dd-49ac-93ec-ff710600e42e

Chicago Manual of Style (16th Edition):

Ciulei, Victor (author). “Cluster-based Analysis of Airline Adherence to Flight Plan: The European Case.” 2020. Masters Thesis, Delft University of Technology. Accessed September 28, 2020. http://resolver.tudelft.nl/uuid:3455449e-04dd-49ac-93ec-ff710600e42e.

MLA Handbook (7th Edition):

Ciulei, Victor (author). “Cluster-based Analysis of Airline Adherence to Flight Plan: The European Case.” 2020. Web. 28 Sep 2020.

Vancouver:

Ciulei V(. Cluster-based Analysis of Airline Adherence to Flight Plan: The European Case. [Internet] [Masters thesis]. Delft University of Technology; 2020. [cited 2020 Sep 28]. Available from: http://resolver.tudelft.nl/uuid:3455449e-04dd-49ac-93ec-ff710600e42e.

Council of Science Editors:

Ciulei V(. Cluster-based Analysis of Airline Adherence to Flight Plan: The European Case. [Masters Thesis]. Delft University of Technology; 2020. Available from: http://resolver.tudelft.nl/uuid:3455449e-04dd-49ac-93ec-ff710600e42e


Universidade Nova

14. Bucker, Thies. Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report.

Degree: 2016, Universidade Nova

Clustering is one of the most frequently applied techniques in machine learning. An overview of the most comon algorithms, problems and solutions is provided in… (more)

Subjects/Keywords: Customer segmentation; Clustering; K-means; Unsupervised learning; Segmentation

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APA (6th Edition):

Bucker, T. (2016). Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report. (Thesis). Universidade Nova. Retrieved from http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/19789

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):

Bucker, Thies. “Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report.” 2016. Thesis, Universidade Nova. Accessed September 28, 2020. http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/19789.

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

MLA Handbook (7th Edition):

Bucker, Thies. “Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report.” 2016. Web. 28 Sep 2020.

Vancouver:

Bucker T. Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report. [Internet] [Thesis]. Universidade Nova; 2016. [cited 2020 Sep 28]. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/19789.

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

Council of Science Editors:

Bucker T. Costumer clustering in the insurance sector by means of unsupervised machine learning : an internship report. [Thesis]. Universidade Nova; 2016. Available from: http://www.rcaap.pt/detail.jsp?id=oai:run.unl.pt:10362/19789

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


Virginia Tech

15. Afzalan, Milad. Building Energy Profile Clustering Based on Energy Consumption Patterns.

Degree: MS, Computer Science and Application, 2020, Virginia Tech

 With the unprecedented amount of data collected by smart meters, we have opportunities to systematically analyze the energy consumption patterns of households. Specifically, through using… (more)

Subjects/Keywords: Clustering; Unsupervised learning; Segmentation; Smart gird; Energy consumption

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APA (6th Edition):

Afzalan, M. (2020). Building Energy Profile Clustering Based on Energy Consumption Patterns. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/99317

Chicago Manual of Style (16th Edition):

Afzalan, Milad. “Building Energy Profile Clustering Based on Energy Consumption Patterns.” 2020. Masters Thesis, Virginia Tech. Accessed September 28, 2020. http://hdl.handle.net/10919/99317.

MLA Handbook (7th Edition):

Afzalan, Milad. “Building Energy Profile Clustering Based on Energy Consumption Patterns.” 2020. Web. 28 Sep 2020.

Vancouver:

Afzalan M. Building Energy Profile Clustering Based on Energy Consumption Patterns. [Internet] [Masters thesis]. Virginia Tech; 2020. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10919/99317.

Council of Science Editors:

Afzalan M. Building Energy Profile Clustering Based on Energy Consumption Patterns. [Masters Thesis]. Virginia Tech; 2020. Available from: http://hdl.handle.net/10919/99317


University of Cape Town

16. Yelibi, Lionel. Introduction to fast Super-Paramagnetic Clustering.

Degree: MSc, Statistical Sciences, 2019, University of Cape Town

 We map stock market interactions to spin models to recover their hierarchical structure using a simulated annealing based Super-Paramagnetic Clustering (SPC) algorithm. This is directly… (more)

Subjects/Keywords: maximum likelihood; Potts Models; unsupervised learning; clustering; maximum entropy

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

APA (6th Edition):

Yelibi, L. (2019). Introduction to fast Super-Paramagnetic Clustering. (Masters Thesis). University of Cape Town. Retrieved from http://hdl.handle.net/11427/31332

Chicago Manual of Style (16th Edition):

Yelibi, Lionel. “Introduction to fast Super-Paramagnetic Clustering.” 2019. Masters Thesis, University of Cape Town. Accessed September 28, 2020. http://hdl.handle.net/11427/31332.

MLA Handbook (7th Edition):

Yelibi, Lionel. “Introduction to fast Super-Paramagnetic Clustering.” 2019. Web. 28 Sep 2020.

Vancouver:

Yelibi L. Introduction to fast Super-Paramagnetic Clustering. [Internet] [Masters thesis]. University of Cape Town; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/11427/31332.

Council of Science Editors:

Yelibi L. Introduction to fast Super-Paramagnetic Clustering. [Masters Thesis]. University of Cape Town; 2019. Available from: http://hdl.handle.net/11427/31332

17. Sun, Lin. Automatic induction of verb classes using clustering.

Degree: PhD, 2013, University of Cambridge

 Verb classifications have attracted a great deal of interest in both linguistics and natural language processing (NLP). They have proved useful for important tasks and… (more)

Subjects/Keywords: Lexical semantics; Verb classification; Verb clustering; Unsupervised learning; Langauge acquisition

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

APA (6th Edition):

Sun, L. (2013). Automatic induction of verb classes using clustering. (Doctoral Dissertation). University of Cambridge. Retrieved from http://www.dspace.cam.ac.uk/handle/1810/244714https://www.repository.cam.ac.uk/bitstream/1810/244714/2/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/3/license_url ; https://www.repository.cam.ac.uk/bitstream/1810/244714/4/license_text ; https://www.repository.cam.ac.uk/bitstream/1810/244714/5/license_rdf ; https://www.repository.cam.ac.uk/bitstream/1810/244714/6/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/7/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/8/thesis.pdf.jpg

Chicago Manual of Style (16th Edition):

Sun, Lin. “Automatic induction of verb classes using clustering.” 2013. Doctoral Dissertation, University of Cambridge. Accessed September 28, 2020. http://www.dspace.cam.ac.uk/handle/1810/244714https://www.repository.cam.ac.uk/bitstream/1810/244714/2/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/3/license_url ; https://www.repository.cam.ac.uk/bitstream/1810/244714/4/license_text ; https://www.repository.cam.ac.uk/bitstream/1810/244714/5/license_rdf ; https://www.repository.cam.ac.uk/bitstream/1810/244714/6/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/7/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/8/thesis.pdf.jpg.

MLA Handbook (7th Edition):

Sun, Lin. “Automatic induction of verb classes using clustering.” 2013. Web. 28 Sep 2020.

Vancouver:

Sun L. Automatic induction of verb classes using clustering. [Internet] [Doctoral dissertation]. University of Cambridge; 2013. [cited 2020 Sep 28]. Available from: http://www.dspace.cam.ac.uk/handle/1810/244714https://www.repository.cam.ac.uk/bitstream/1810/244714/2/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/3/license_url ; https://www.repository.cam.ac.uk/bitstream/1810/244714/4/license_text ; https://www.repository.cam.ac.uk/bitstream/1810/244714/5/license_rdf ; https://www.repository.cam.ac.uk/bitstream/1810/244714/6/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/7/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/8/thesis.pdf.jpg.

Council of Science Editors:

Sun L. Automatic induction of verb classes using clustering. [Doctoral Dissertation]. University of Cambridge; 2013. Available from: http://www.dspace.cam.ac.uk/handle/1810/244714https://www.repository.cam.ac.uk/bitstream/1810/244714/2/license.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/3/license_url ; https://www.repository.cam.ac.uk/bitstream/1810/244714/4/license_text ; https://www.repository.cam.ac.uk/bitstream/1810/244714/5/license_rdf ; https://www.repository.cam.ac.uk/bitstream/1810/244714/6/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/7/thesis.pdf.txt ; https://www.repository.cam.ac.uk/bitstream/1810/244714/8/thesis.pdf.jpg


University of Manitoba

18. Ocaña Macias Mariano. Vascular plaque detection using texture based segmentation of optical coherence tomography images.

Degree: Electrical and Computer Engineering, 2015, University of Manitoba

 Abstract Cardiovascular disease is one of the leading causes of death in Canada. Atherosclerosis is considered the primary cause for cardiovascular disease. Optical coherence tomography… (more)

Subjects/Keywords: Optical Coherence Tomography; Atherosclerosis; texture based Segmentation; unsupervised clustering methods

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

APA (6th Edition):

Mariano, O. M. (2015). Vascular plaque detection using texture based segmentation of optical coherence tomography images. (Masters Thesis). University of Manitoba. Retrieved from http://hdl.handle.net/1993/30786

Chicago Manual of Style (16th Edition):

Mariano, Ocaña Macias. “Vascular plaque detection using texture based segmentation of optical coherence tomography images.” 2015. Masters Thesis, University of Manitoba. Accessed September 28, 2020. http://hdl.handle.net/1993/30786.

MLA Handbook (7th Edition):

Mariano, Ocaña Macias. “Vascular plaque detection using texture based segmentation of optical coherence tomography images.” 2015. Web. 28 Sep 2020.

Vancouver:

Mariano OM. Vascular plaque detection using texture based segmentation of optical coherence tomography images. [Internet] [Masters thesis]. University of Manitoba; 2015. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1993/30786.

Council of Science Editors:

Mariano OM. Vascular plaque detection using texture based segmentation of optical coherence tomography images. [Masters Thesis]. University of Manitoba; 2015. Available from: http://hdl.handle.net/1993/30786


Northeastern University

19. Guan, Yue. Bayesian models for unsupervised feature selection.

Degree: PhD, Department of Electrical and Computer Engineering, 2012, Northeastern University

 This dissertation focuses on developing probabilistic models for unsupervised feature selection. High-dimensional data often contain irrelevant and redundant features, which can hurt learning algorithms. One… (more)

Subjects/Keywords: Bayesian Model; clustering; Feature selection; PCA; unsupervised learning; Computer Engineering

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

APA (6th Edition):

Guan, Y. (2012). Bayesian models for unsupervised feature selection. (Doctoral Dissertation). Northeastern University. Retrieved from http://hdl.handle.net/2047/d20002991

Chicago Manual of Style (16th Edition):

Guan, Yue. “Bayesian models for unsupervised feature selection.” 2012. Doctoral Dissertation, Northeastern University. Accessed September 28, 2020. http://hdl.handle.net/2047/d20002991.

MLA Handbook (7th Edition):

Guan, Yue. “Bayesian models for unsupervised feature selection.” 2012. Web. 28 Sep 2020.

Vancouver:

Guan Y. Bayesian models for unsupervised feature selection. [Internet] [Doctoral dissertation]. Northeastern University; 2012. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2047/d20002991.

Council of Science Editors:

Guan Y. Bayesian models for unsupervised feature selection. [Doctoral Dissertation]. Northeastern University; 2012. Available from: http://hdl.handle.net/2047/d20002991


Iowa State University

20. Berry, Nicholas S. Extending K-means.

Degree: 2019, Iowa State University

 In the unsupervised learning setting, where data labels are not available and few constraints are put on data structure before analysis, having a robust procedure… (more)

Subjects/Keywords: clustering; handwriting; K-means; TiK-means; unsupervised; Statistics and Probability

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

APA (6th Edition):

Berry, N. S. (2019). Extending K-means. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16973

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):

Berry, Nicholas S. “Extending K-means.” 2019. Thesis, Iowa State University. Accessed September 28, 2020. https://lib.dr.iastate.edu/etd/16973.

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

MLA Handbook (7th Edition):

Berry, Nicholas S. “Extending K-means.” 2019. Web. 28 Sep 2020.

Vancouver:

Berry NS. Extending K-means. [Internet] [Thesis]. Iowa State University; 2019. [cited 2020 Sep 28]. Available from: https://lib.dr.iastate.edu/etd/16973.

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

Council of Science Editors:

Berry NS. Extending K-means. [Thesis]. Iowa State University; 2019. Available from: https://lib.dr.iastate.edu/etd/16973

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

21. Curtis, Jessica. Class discovery via feature selection in unsupervised settings.

Degree: PhD, Mathematics & Statistics, 2016, Boston University

 Identifying genes linked to the appearance of certain types of cancers and their phenotypes is a well-known and challenging problem in bioinformatics. Discovering marker genes… (more)

Subjects/Keywords: Statistics; Class discovery; Clustering; Feature selection; Gene expression; Higher criticism; Unsupervised

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

APA (6th Edition):

Curtis, J. (2016). Class discovery via feature selection in unsupervised settings. (Doctoral Dissertation). Boston University. Retrieved from http://hdl.handle.net/2144/14548

Chicago Manual of Style (16th Edition):

Curtis, Jessica. “Class discovery via feature selection in unsupervised settings.” 2016. Doctoral Dissertation, Boston University. Accessed September 28, 2020. http://hdl.handle.net/2144/14548.

MLA Handbook (7th Edition):

Curtis, Jessica. “Class discovery via feature selection in unsupervised settings.” 2016. Web. 28 Sep 2020.

Vancouver:

Curtis J. Class discovery via feature selection in unsupervised settings. [Internet] [Doctoral dissertation]. Boston University; 2016. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2144/14548.

Council of Science Editors:

Curtis J. Class discovery via feature selection in unsupervised settings. [Doctoral Dissertation]. Boston University; 2016. Available from: http://hdl.handle.net/2144/14548


University of Illinois – Urbana-Champaign

22. Harish, Abhishek. Automatically extracting interaction and app data from mobile application traces.

Degree: MS, Computer Science, 2016, University of Illinois – Urbana-Champaign

 In this research, we used an existing system to collect mobile interaction traces and extract meaningful information in terms of interaction data, apps, and layout… (more)

Subjects/Keywords: Human-computer interaction (HCI); Interaction Mining; Unsupervised Clustering; Element Extraction

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

APA (6th Edition):

Harish, A. (2016). Automatically extracting interaction and app data from mobile application traces. (Thesis). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/90757

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):

Harish, Abhishek. “Automatically extracting interaction and app data from mobile application traces.” 2016. Thesis, University of Illinois – Urbana-Champaign. Accessed September 28, 2020. http://hdl.handle.net/2142/90757.

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

MLA Handbook (7th Edition):

Harish, Abhishek. “Automatically extracting interaction and app data from mobile application traces.” 2016. Web. 28 Sep 2020.

Vancouver:

Harish A. Automatically extracting interaction and app data from mobile application traces. [Internet] [Thesis]. University of Illinois – Urbana-Champaign; 2016. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2142/90757.

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

Council of Science Editors:

Harish A. Automatically extracting interaction and app data from mobile application traces. [Thesis]. University of Illinois – Urbana-Champaign; 2016. Available from: http://hdl.handle.net/2142/90757

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


University of Minnesota

23. Yang, Bo. Unsupervised Learning of Latent Structure from Linear and Nonlinear Measurements.

Degree: PhD, Electrical Engineering, 2019, University of Minnesota

 The past few decades have seen a rapid expansion of our digital world. While early dwellers of the Internet exchanged simple text messages via email,… (more)

Subjects/Keywords: clustering; dimensionality reduction; identifiability; matrix factorization; nonlinearity; unsupervised learning

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

APA (6th Edition):

Yang, B. (2019). Unsupervised Learning of Latent Structure from Linear and Nonlinear Measurements. (Doctoral Dissertation). University of Minnesota. Retrieved from http://hdl.handle.net/11299/206423

Chicago Manual of Style (16th Edition):

Yang, Bo. “Unsupervised Learning of Latent Structure from Linear and Nonlinear Measurements.” 2019. Doctoral Dissertation, University of Minnesota. Accessed September 28, 2020. http://hdl.handle.net/11299/206423.

MLA Handbook (7th Edition):

Yang, Bo. “Unsupervised Learning of Latent Structure from Linear and Nonlinear Measurements.” 2019. Web. 28 Sep 2020.

Vancouver:

Yang B. Unsupervised Learning of Latent Structure from Linear and Nonlinear Measurements. [Internet] [Doctoral dissertation]. University of Minnesota; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/11299/206423.

Council of Science Editors:

Yang B. Unsupervised Learning of Latent Structure from Linear and Nonlinear Measurements. [Doctoral Dissertation]. University of Minnesota; 2019. Available from: http://hdl.handle.net/11299/206423


University of British Columbia

24. Koepke, Hoyt Adam. Bayesian cluster validation .

Degree: 2008, University of British Columbia

 We propose a novel framework based on Bayesian principles for validating clusterings and present efficient algorithms for use with centroid or exemplar based clustering solutions.… (more)

Subjects/Keywords: Clustering; Cluster validation; Unsupervised learning

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

APA (6th Edition):

Koepke, H. A. (2008). Bayesian cluster validation . (Thesis). University of British Columbia. Retrieved from http://hdl.handle.net/2429/1496

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):

Koepke, Hoyt Adam. “Bayesian cluster validation .” 2008. Thesis, University of British Columbia. Accessed September 28, 2020. http://hdl.handle.net/2429/1496.

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

MLA Handbook (7th Edition):

Koepke, Hoyt Adam. “Bayesian cluster validation .” 2008. Web. 28 Sep 2020.

Vancouver:

Koepke HA. Bayesian cluster validation . [Internet] [Thesis]. University of British Columbia; 2008. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2429/1496.

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

Council of Science Editors:

Koepke HA. Bayesian cluster validation . [Thesis]. University of British Columbia; 2008. Available from: http://hdl.handle.net/2429/1496

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


University of Cambridge

25. Sun, Lin. Automatic induction of verb classes using clustering.

Degree: PhD, 2013, University of Cambridge

 Verb classifications have attracted a great deal of interest in both linguistics and natural language processing (NLP). They have proved useful for important tasks and… (more)

Subjects/Keywords: 415; Lexical semantics; Verb classification; Verb clustering; Unsupervised learning; Langauge acquisition

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

APA (6th Edition):

Sun, L. (2013). Automatic induction of verb classes using clustering. (Doctoral Dissertation). University of Cambridge. Retrieved from https://www.repository.cam.ac.uk/handle/1810/244714 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590207

Chicago Manual of Style (16th Edition):

Sun, Lin. “Automatic induction of verb classes using clustering.” 2013. Doctoral Dissertation, University of Cambridge. Accessed September 28, 2020. https://www.repository.cam.ac.uk/handle/1810/244714 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590207.

MLA Handbook (7th Edition):

Sun, Lin. “Automatic induction of verb classes using clustering.” 2013. Web. 28 Sep 2020.

Vancouver:

Sun L. Automatic induction of verb classes using clustering. [Internet] [Doctoral dissertation]. University of Cambridge; 2013. [cited 2020 Sep 28]. Available from: https://www.repository.cam.ac.uk/handle/1810/244714 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590207.

Council of Science Editors:

Sun L. Automatic induction of verb classes using clustering. [Doctoral Dissertation]. University of Cambridge; 2013. Available from: https://www.repository.cam.ac.uk/handle/1810/244714 ; http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.590207


The Ohio State University

26. Eldridge, Justin, Eldridge. Clustering Consistently.

Degree: PhD, Computer Science and Engineering, 2017, The Ohio State University

Clustering is the task of organizing data into natural groups, or clusters. A central goal in developing a theory of clustering is the derivation of… (more)

Subjects/Keywords: Computer Science; Statistics; Artificial Intelligence; machine learning; unsupervised learning; statistical learning; clustering; graphon; mergeon; density cluster tree; hierarchical clustering

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

APA (6th Edition):

Eldridge, Justin, E. (2017). Clustering Consistently. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1512070374903249

Chicago Manual of Style (16th Edition):

Eldridge, Justin, Eldridge. “Clustering Consistently.” 2017. Doctoral Dissertation, The Ohio State University. Accessed September 28, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1512070374903249.

MLA Handbook (7th Edition):

Eldridge, Justin, Eldridge. “Clustering Consistently.” 2017. Web. 28 Sep 2020.

Vancouver:

Eldridge, Justin E. Clustering Consistently. [Internet] [Doctoral dissertation]. The Ohio State University; 2017. [cited 2020 Sep 28]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1512070374903249.

Council of Science Editors:

Eldridge, Justin E. Clustering Consistently. [Doctoral Dissertation]. The Ohio State University; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1512070374903249


Halmstad University

27. Sweidan, Dirar. A General Framework for Discovering Multiple Data Groupings.

Degree: Information Technology, 2018, Halmstad University

Clustering helps users gain insights from their data by discovering hidden structures in an unsupervised way. Unlike classification tasks that are evaluated using well-defined… (more)

Subjects/Keywords: machine learning; unsupervised learning; data mining; clustering; multiple-clusterings; clustering algorithm; Engineering and Technology; Teknik och teknologier; Computer Systems; Datorsystem

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

APA (6th Edition):

Sweidan, D. (2018). A General Framework for Discovering Multiple Data Groupings. (Thesis). Halmstad University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38047

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):

Sweidan, Dirar. “A General Framework for Discovering Multiple Data Groupings.” 2018. Thesis, Halmstad University. Accessed September 28, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38047.

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

MLA Handbook (7th Edition):

Sweidan, Dirar. “A General Framework for Discovering Multiple Data Groupings.” 2018. Web. 28 Sep 2020.

Vancouver:

Sweidan D. A General Framework for Discovering Multiple Data Groupings. [Internet] [Thesis]. Halmstad University; 2018. [cited 2020 Sep 28]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38047.

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

Council of Science Editors:

Sweidan D. A General Framework for Discovering Multiple Data Groupings. [Thesis]. Halmstad University; 2018. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:hh:diva-38047

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

28. Chiapino, Maël. Apprentissage de structures dans les valeurs extrêmes en grande dimension : Discovering patterns in high-dimensional extremes.

Degree: Docteur es, Signal et Images, 2018, Paris, ENST

Nous présentons et étudions des méthodes d’apprentissage non-supervisé de phénomènes extrêmes multivariés en grande dimension. Dans le cas où chacune des distributions marginales d’un vecteur… (more)

Subjects/Keywords: Théorie des valeurs extrêmes; Apprentissage non-supervisé; Réduction de dimension; Clustering; Extreme value theory; Unsupervised learning; Dimension reduction; Clustering

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

APA (6th Edition):

Chiapino, M. (2018). Apprentissage de structures dans les valeurs extrêmes en grande dimension : Discovering patterns in high-dimensional extremes. (Doctoral Dissertation). Paris, ENST. Retrieved from http://www.theses.fr/2018ENST0035

Chicago Manual of Style (16th Edition):

Chiapino, Maël. “Apprentissage de structures dans les valeurs extrêmes en grande dimension : Discovering patterns in high-dimensional extremes.” 2018. Doctoral Dissertation, Paris, ENST. Accessed September 28, 2020. http://www.theses.fr/2018ENST0035.

MLA Handbook (7th Edition):

Chiapino, Maël. “Apprentissage de structures dans les valeurs extrêmes en grande dimension : Discovering patterns in high-dimensional extremes.” 2018. Web. 28 Sep 2020.

Vancouver:

Chiapino M. Apprentissage de structures dans les valeurs extrêmes en grande dimension : Discovering patterns in high-dimensional extremes. [Internet] [Doctoral dissertation]. Paris, ENST; 2018. [cited 2020 Sep 28]. Available from: http://www.theses.fr/2018ENST0035.

Council of Science Editors:

Chiapino M. Apprentissage de structures dans les valeurs extrêmes en grande dimension : Discovering patterns in high-dimensional extremes. [Doctoral Dissertation]. Paris, ENST; 2018. Available from: http://www.theses.fr/2018ENST0035


Georgia Tech

29. Gupta, Pramod. Robust clustering algorithms.

Degree: MS, Computing, 2011, Georgia Tech

 One of the most widely used techniques for data clustering is agglomerative clustering. Such algorithms have been long used across any different fields ranging from… (more)

Subjects/Keywords: Robust algorithms; Hierarchical clustering; Unsupervised learning; Clustering; Machine learning; Cluster analysis; Cluster analysis Computer programs; Algorithms

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

APA (6th Edition):

Gupta, P. (2011). Robust clustering algorithms. (Masters Thesis). Georgia Tech. Retrieved from http://hdl.handle.net/1853/39553

Chicago Manual of Style (16th Edition):

Gupta, Pramod. “Robust clustering algorithms.” 2011. Masters Thesis, Georgia Tech. Accessed September 28, 2020. http://hdl.handle.net/1853/39553.

MLA Handbook (7th Edition):

Gupta, Pramod. “Robust clustering algorithms.” 2011. Web. 28 Sep 2020.

Vancouver:

Gupta P. Robust clustering algorithms. [Internet] [Masters thesis]. Georgia Tech; 2011. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1853/39553.

Council of Science Editors:

Gupta P. Robust clustering algorithms. [Masters Thesis]. Georgia Tech; 2011. Available from: http://hdl.handle.net/1853/39553


University of Cincinnati

30. Awodokun, Olugbenga. Classification of Patterns in Streaming Data Using Clustering Signatures.

Degree: MS, Engineering and Applied Science: Electrical Engineering, 2017, University of Cincinnati

 Streaming datasets often pose a myriad of challenges for machine learning algorithms, some of which include insufficient storage and changes in the underlying distributions of… (more)

Subjects/Keywords: Computer Science; data mining; hierarchical clustering; unsupervised learning; data analytics; machine learning; intrusion detection

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

APA (6th Edition):

Awodokun, O. (2017). Classification of Patterns in Streaming Data Using Clustering Signatures. (Masters Thesis). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504880155623189

Chicago Manual of Style (16th Edition):

Awodokun, Olugbenga. “Classification of Patterns in Streaming Data Using Clustering Signatures.” 2017. Masters Thesis, University of Cincinnati. Accessed September 28, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504880155623189.

MLA Handbook (7th Edition):

Awodokun, Olugbenga. “Classification of Patterns in Streaming Data Using Clustering Signatures.” 2017. Web. 28 Sep 2020.

Vancouver:

Awodokun O. Classification of Patterns in Streaming Data Using Clustering Signatures. [Internet] [Masters thesis]. University of Cincinnati; 2017. [cited 2020 Sep 28]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504880155623189.

Council of Science Editors:

Awodokun O. Classification of Patterns in Streaming Data Using Clustering Signatures. [Masters Thesis]. University of Cincinnati; 2017. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1504880155623189

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