Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

Sorted by: relevance · author · university · dateNew search

You searched for subject:(Data Clustering). Showing records 1 – 30 of 743 total matches.

[1] [2] [3] [4] [5] … [25]

Search Limiters

Last 2 Years | English Only

Degrees

Levels

Languages

Country

▼ Search Limiters


University of Alberta

1. Wubie, Berhanu A. Clustering Survival Data using Random Forest and Persistent Homology.

Degree: MS, Department of Mathematical and Statistical Sciences, 2016, University of Alberta

 Survival data is mostly analyzed using Cox proportional hazards model to identify factors associated with survival time of patients. However recently random survival forest (RSF),… (more)

Subjects/Keywords: Clustering; Survival; Data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Wubie, B. A. (2016). Clustering Survival Data using Random Forest and Persistent Homology. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/c8w32r580b

Chicago Manual of Style (16th Edition):

Wubie, Berhanu A. “Clustering Survival Data using Random Forest and Persistent Homology.” 2016. Masters Thesis, University of Alberta. Accessed September 28, 2020. https://era.library.ualberta.ca/files/c8w32r580b.

MLA Handbook (7th Edition):

Wubie, Berhanu A. “Clustering Survival Data using Random Forest and Persistent Homology.” 2016. Web. 28 Sep 2020.

Vancouver:

Wubie BA. Clustering Survival Data using Random Forest and Persistent Homology. [Internet] [Masters thesis]. University of Alberta; 2016. [cited 2020 Sep 28]. Available from: https://era.library.ualberta.ca/files/c8w32r580b.

Council of Science Editors:

Wubie BA. Clustering Survival Data using Random Forest and Persistent Homology. [Masters Thesis]. University of Alberta; 2016. Available from: https://era.library.ualberta.ca/files/c8w32r580b


University of Southern California

2. Lim, Jongwoo. An efficient approach to clustering datasets with mixed type attributes in data mining.

Degree: PhD, Computer Science, 2013, University of Southern California

 We propose an efficient approach to clustering datasets with mixed type attributes (both numerical and categorical), while minimizing information loss during clustering. Real world datasets… (more)

Subjects/Keywords: clustering; data mining

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lim, J. (2013). An efficient approach to clustering datasets with mixed type attributes in data mining. (Doctoral Dissertation). University of Southern California. Retrieved from http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/333350/rec/695

Chicago Manual of Style (16th Edition):

Lim, Jongwoo. “An efficient approach to clustering datasets with mixed type attributes in data mining.” 2013. Doctoral Dissertation, University of Southern California. Accessed September 28, 2020. http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/333350/rec/695.

MLA Handbook (7th Edition):

Lim, Jongwoo. “An efficient approach to clustering datasets with mixed type attributes in data mining.” 2013. Web. 28 Sep 2020.

Vancouver:

Lim J. An efficient approach to clustering datasets with mixed type attributes in data mining. [Internet] [Doctoral dissertation]. University of Southern California; 2013. [cited 2020 Sep 28]. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/333350/rec/695.

Council of Science Editors:

Lim J. An efficient approach to clustering datasets with mixed type attributes in data mining. [Doctoral Dissertation]. University of Southern California; 2013. Available from: http://digitallibrary.usc.edu/cdm/compoundobject/collection/p15799coll3/id/333350/rec/695


Rochester Institute of Technology

3. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 September 28, 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. 28 Sep 2020.

Vancouver:

Green NS. Evolutionary spectral co-clustering. [Internet] [Thesis]. Rochester Institute of Technology; 2010. [cited 2020 Sep 28]. 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


Rice University

4. Yang, Yuchen. Convergence of K-indicators Clustering with Alternating Projection Algorithms.

Degree: MA, Engineering, 2017, Rice University

Data clustering is a fundamental unsupervised machine learning problem, and the most widely used method of data clustering over the decades is k-means. Recently, a… (more)

Subjects/Keywords: Data clustering; Alternating Projection

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Yang, Y. (2017). Convergence of K-indicators Clustering with Alternating Projection Algorithms. (Masters Thesis). Rice University. Retrieved from http://hdl.handle.net/1911/105482

Chicago Manual of Style (16th Edition):

Yang, Yuchen. “Convergence of K-indicators Clustering with Alternating Projection Algorithms.” 2017. Masters Thesis, Rice University. Accessed September 28, 2020. http://hdl.handle.net/1911/105482.

MLA Handbook (7th Edition):

Yang, Yuchen. “Convergence of K-indicators Clustering with Alternating Projection Algorithms.” 2017. Web. 28 Sep 2020.

Vancouver:

Yang Y. Convergence of K-indicators Clustering with Alternating Projection Algorithms. [Internet] [Masters thesis]. Rice University; 2017. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1911/105482.

Council of Science Editors:

Yang Y. Convergence of K-indicators Clustering with Alternating Projection Algorithms. [Masters Thesis]. Rice University; 2017. Available from: http://hdl.handle.net/1911/105482


Victoria University of Wellington

5. Fujita, Yuki. Clustering and Classification in Fisheries.

Degree: 2016, Victoria University of Wellington

 This goal of this research is to investigate associations between presences of fish species, space, and time in a selected set of areas in New… (more)

Subjects/Keywords: Clustering; Fisheries; Categorical data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Fujita, Y. (2016). Clustering and Classification in Fisheries. (Masters Thesis). Victoria University of Wellington. Retrieved from http://hdl.handle.net/10063/5321

Chicago Manual of Style (16th Edition):

Fujita, Yuki. “Clustering and Classification in Fisheries.” 2016. Masters Thesis, Victoria University of Wellington. Accessed September 28, 2020. http://hdl.handle.net/10063/5321.

MLA Handbook (7th Edition):

Fujita, Yuki. “Clustering and Classification in Fisheries.” 2016. Web. 28 Sep 2020.

Vancouver:

Fujita Y. Clustering and Classification in Fisheries. [Internet] [Masters thesis]. Victoria University of Wellington; 2016. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10063/5321.

Council of Science Editors:

Fujita Y. Clustering and Classification in Fisheries. [Masters Thesis]. Victoria University of Wellington; 2016. Available from: http://hdl.handle.net/10063/5321


California State University – Sacramento

6. Reggad, Hind. Flowpeaks analysis for flow cytometry data using MATLAB.

Degree: MS, Electrical and Electronic Engineering, 2014, California State University – Sacramento

 For flow cytometry analysis, data clustering has been used extensively to partition cells into distinct groups by measuring fluorescence intensity. The traditional techniques of identifying… (more)

Subjects/Keywords: K-means algorithm; Clustering data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Reggad, H. (2014). Flowpeaks analysis for flow cytometry data using MATLAB. (Masters Thesis). California State University – Sacramento. Retrieved from http://hdl.handle.net/10211.9/2349

Chicago Manual of Style (16th Edition):

Reggad, Hind. “Flowpeaks analysis for flow cytometry data using MATLAB.” 2014. Masters Thesis, California State University – Sacramento. Accessed September 28, 2020. http://hdl.handle.net/10211.9/2349.

MLA Handbook (7th Edition):

Reggad, Hind. “Flowpeaks analysis for flow cytometry data using MATLAB.” 2014. Web. 28 Sep 2020.

Vancouver:

Reggad H. Flowpeaks analysis for flow cytometry data using MATLAB. [Internet] [Masters thesis]. California State University – Sacramento; 2014. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10211.9/2349.

Council of Science Editors:

Reggad H. Flowpeaks analysis for flow cytometry data using MATLAB. [Masters Thesis]. California State University – Sacramento; 2014. Available from: http://hdl.handle.net/10211.9/2349


Anna University

7. Hari prasad D. Integrated framework for Visualized exploratory data Clustering and pattern extraction in Mixed data;.

Degree: Integrated framework for Visualized exploratory data Clustering and pattern extraction in Mixed data, 2015, Anna University

Data mining is a form of analyzing the data which relates the newlinetechniques from fields like statistics machine learning databases artificial newlineintelligence etc Clustering is… (more)

Subjects/Keywords: Artificial intelligence; Data clustering; Data mining

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

D, H. p. (2015). Integrated framework for Visualized exploratory data Clustering and pattern extraction in Mixed data;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/55926

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):

D, Hari prasad. “Integrated framework for Visualized exploratory data Clustering and pattern extraction in Mixed data;.” 2015. Thesis, Anna University. Accessed September 28, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/55926.

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

MLA Handbook (7th Edition):

D, Hari prasad. “Integrated framework for Visualized exploratory data Clustering and pattern extraction in Mixed data;.” 2015. Web. 28 Sep 2020.

Vancouver:

D Hp. Integrated framework for Visualized exploratory data Clustering and pattern extraction in Mixed data;. [Internet] [Thesis]. Anna University; 2015. [cited 2020 Sep 28]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/55926.

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

Council of Science Editors:

D Hp. Integrated framework for Visualized exploratory data Clustering and pattern extraction in Mixed data;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/55926

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


Deakin University

8. Huynh, Viet Huu. Towards scalable Bayesian nonparametric methods for data analytics.

Degree: School of Information Technology, 2017, Deakin University

 Resorting big data to actionable information involves dealing with four dimensions of challenges in big data (called four V’s): volume, variety, velocity, veracity. In this… (more)

Subjects/Keywords: big data; data mining; multi-level clustering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Huynh, V. H. (2017). Towards scalable Bayesian nonparametric methods for data analytics. (Thesis). Deakin University. Retrieved from http://hdl.handle.net/10536/DRO/DU:30103238

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):

Huynh, Viet Huu. “Towards scalable Bayesian nonparametric methods for data analytics.” 2017. Thesis, Deakin University. Accessed September 28, 2020. http://hdl.handle.net/10536/DRO/DU:30103238.

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

MLA Handbook (7th Edition):

Huynh, Viet Huu. “Towards scalable Bayesian nonparametric methods for data analytics.” 2017. Web. 28 Sep 2020.

Vancouver:

Huynh VH. Towards scalable Bayesian nonparametric methods for data analytics. [Internet] [Thesis]. Deakin University; 2017. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10536/DRO/DU:30103238.

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

Council of Science Editors:

Huynh VH. Towards scalable Bayesian nonparametric methods for data analytics. [Thesis]. Deakin University; 2017. Available from: http://hdl.handle.net/10536/DRO/DU:30103238

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


Delft University of Technology

9. Van der Ende, D.J. (author). Accelerated Mean Shift for static and streaming environments.

Degree: 2015, Delft University of Technology

Mean Shift is a well-known clustering algorithm that has attractive properties such as the ability to find non convex and local clusters even in high… (more)

Subjects/Keywords: mean shift; data stream clustering; data mining

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Van der Ende, D. J. (. (2015). Accelerated Mean Shift for static and streaming environments. (Masters Thesis). Delft University of Technology. Retrieved from http://resolver.tudelft.nl/uuid:7fdb578a-a3e3-430c-b257-c85bfc45d3d9

Chicago Manual of Style (16th Edition):

Van der Ende, D J (author). “Accelerated Mean Shift for static and streaming environments.” 2015. Masters Thesis, Delft University of Technology. Accessed September 28, 2020. http://resolver.tudelft.nl/uuid:7fdb578a-a3e3-430c-b257-c85bfc45d3d9.

MLA Handbook (7th Edition):

Van der Ende, D J (author). “Accelerated Mean Shift for static and streaming environments.” 2015. Web. 28 Sep 2020.

Vancouver:

Van der Ende DJ(. Accelerated Mean Shift for static and streaming environments. [Internet] [Masters thesis]. Delft University of Technology; 2015. [cited 2020 Sep 28]. Available from: http://resolver.tudelft.nl/uuid:7fdb578a-a3e3-430c-b257-c85bfc45d3d9.

Council of Science Editors:

Van der Ende DJ(. Accelerated Mean Shift for static and streaming environments. [Masters Thesis]. Delft University of Technology; 2015. Available from: http://resolver.tudelft.nl/uuid:7fdb578a-a3e3-430c-b257-c85bfc45d3d9


University of Louisville

10. Hawwash, Basheer, 1984-. Stream-dashboard : a big data stream clustering framework with applications to social media streams.

Degree: PhD, 2013, University of Louisville

Data mining is concerned with detecting patterns of data in raw datasets, which are then used to unearth knowledge that might not have been discovered… (more)

Subjects/Keywords: Data mining; Tracking changes; Machine learning; Clustering; Data stream clustering; Twitter

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Hawwash, Basheer, 1. (2013). Stream-dashboard : a big data stream clustering framework with applications to social media streams. (Doctoral Dissertation). University of Louisville. Retrieved from 10.18297/etd/587 ; https://ir.library.louisville.edu/etd/587

Chicago Manual of Style (16th Edition):

Hawwash, Basheer, 1984-. “Stream-dashboard : a big data stream clustering framework with applications to social media streams.” 2013. Doctoral Dissertation, University of Louisville. Accessed September 28, 2020. 10.18297/etd/587 ; https://ir.library.louisville.edu/etd/587.

MLA Handbook (7th Edition):

Hawwash, Basheer, 1984-. “Stream-dashboard : a big data stream clustering framework with applications to social media streams.” 2013. Web. 28 Sep 2020.

Vancouver:

Hawwash, Basheer 1. Stream-dashboard : a big data stream clustering framework with applications to social media streams. [Internet] [Doctoral dissertation]. University of Louisville; 2013. [cited 2020 Sep 28]. Available from: 10.18297/etd/587 ; https://ir.library.louisville.edu/etd/587.

Council of Science Editors:

Hawwash, Basheer 1. Stream-dashboard : a big data stream clustering framework with applications to social media streams. [Doctoral Dissertation]. University of Louisville; 2013. Available from: 10.18297/etd/587 ; https://ir.library.louisville.edu/etd/587

11. Ribeiro, Swen. Induction non-supervisée de schémas d’évènements à partir de textes journalistiques : Unsupervised event schemas induction from journalistic texts.

Degree: Docteur es, Informatique, 2020, université Paris-Saclay

L'événement est un concept central dans plusieurs tâches du Traitement Automatique des Langues, en dépit de l'absence d'une définition unifiée de ce que recouvre cette… (more)

Subjects/Keywords: Recherche d’Information; Data-journalisme; Clustering; Information Retrieval; Data-journalism; Clustering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Ribeiro, S. (2020). Induction non-supervisée de schémas d’évènements à partir de textes journalistiques : Unsupervised event schemas induction from journalistic texts. (Doctoral Dissertation). université Paris-Saclay. Retrieved from http://www.theses.fr/2020UPASS059

Chicago Manual of Style (16th Edition):

Ribeiro, Swen. “Induction non-supervisée de schémas d’évènements à partir de textes journalistiques : Unsupervised event schemas induction from journalistic texts.” 2020. Doctoral Dissertation, université Paris-Saclay. Accessed September 28, 2020. http://www.theses.fr/2020UPASS059.

MLA Handbook (7th Edition):

Ribeiro, Swen. “Induction non-supervisée de schémas d’évènements à partir de textes journalistiques : Unsupervised event schemas induction from journalistic texts.” 2020. Web. 28 Sep 2020.

Vancouver:

Ribeiro S. Induction non-supervisée de schémas d’évènements à partir de textes journalistiques : Unsupervised event schemas induction from journalistic texts. [Internet] [Doctoral dissertation]. université Paris-Saclay; 2020. [cited 2020 Sep 28]. Available from: http://www.theses.fr/2020UPASS059.

Council of Science Editors:

Ribeiro S. Induction non-supervisée de schémas d’évènements à partir de textes journalistiques : Unsupervised event schemas induction from journalistic texts. [Doctoral Dissertation]. université Paris-Saclay; 2020. Available from: http://www.theses.fr/2020UPASS059


Universidade do Minho

12. Oliveira, João Ricardo Leite Mota. Spatio-temporal SNN : integrating time and space in the clustering process .

Degree: 2013, Universidade do Minho

 Spatio-temporal clustering is a new subfield of data mining that is increasingly gaining scientific attention due to the technical advances of location-based or environmental devices… (more)

Subjects/Keywords: Clustering; Density-based clustering; Spatio-temporal data; Distance function; Spatio-temporal clustering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Oliveira, J. R. L. M. (2013). Spatio-temporal SNN : integrating time and space in the clustering process . (Masters Thesis). Universidade do Minho. Retrieved from http://hdl.handle.net/1822/29459

Chicago Manual of Style (16th Edition):

Oliveira, João Ricardo Leite Mota. “Spatio-temporal SNN : integrating time and space in the clustering process .” 2013. Masters Thesis, Universidade do Minho. Accessed September 28, 2020. http://hdl.handle.net/1822/29459.

MLA Handbook (7th Edition):

Oliveira, João Ricardo Leite Mota. “Spatio-temporal SNN : integrating time and space in the clustering process .” 2013. Web. 28 Sep 2020.

Vancouver:

Oliveira JRLM. Spatio-temporal SNN : integrating time and space in the clustering process . [Internet] [Masters thesis]. Universidade do Minho; 2013. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1822/29459.

Council of Science Editors:

Oliveira JRLM. Spatio-temporal SNN : integrating time and space in the clustering process . [Masters Thesis]. Universidade do Minho; 2013. Available from: http://hdl.handle.net/1822/29459


NSYSU

13. Lin, Tsung-hsien. An Elastic Net Algorithm for Automatic Clustering.

Degree: Master, Computer Science and Engineering, 2014, NSYSU

Clustering has always been playing a vital role in many different disciplines because it is an important tool for analyzing a set of unknown input… (more)

Subjects/Keywords: automatic clustering; number of clusters; clustering; elastic net clustering algorithm; non-linearly separable data

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lin, T. (2014). An Elastic Net Algorithm for Automatic Clustering. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628114-161349

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):

Lin, Tsung-hsien. “An Elastic Net Algorithm for Automatic Clustering.” 2014. Thesis, NSYSU. Accessed September 28, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628114-161349.

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

MLA Handbook (7th Edition):

Lin, Tsung-hsien. “An Elastic Net Algorithm for Automatic Clustering.” 2014. Web. 28 Sep 2020.

Vancouver:

Lin T. An Elastic Net Algorithm for Automatic Clustering. [Internet] [Thesis]. NSYSU; 2014. [cited 2020 Sep 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628114-161349.

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

Council of Science Editors:

Lin T. An Elastic Net Algorithm for Automatic Clustering. [Thesis]. NSYSU; 2014. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0628114-161349

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


Kansas State University

14. Luo, Jianmei. Threshold clustering for massive data.

Degree: PhD, Department of Statistics, 2019, Kansas State University

 Statistical clustering is the process of partitioning objects into clusters so that units within the same cluster have similar characteristics. Threshold clustering (TC) is a… (more)

Subjects/Keywords: Threshold Clustering; Hybridized Clustering; Instance Selection; Hierarchical Clustering; Massive Data; Approximation Algorithm

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Luo, J. (2019). Threshold clustering for massive data. (Doctoral Dissertation). Kansas State University. Retrieved from http://hdl.handle.net/2097/39823

Chicago Manual of Style (16th Edition):

Luo, Jianmei. “Threshold clustering for massive data.” 2019. Doctoral Dissertation, Kansas State University. Accessed September 28, 2020. http://hdl.handle.net/2097/39823.

MLA Handbook (7th Edition):

Luo, Jianmei. “Threshold clustering for massive data.” 2019. Web. 28 Sep 2020.

Vancouver:

Luo J. Threshold clustering for massive data. [Internet] [Doctoral dissertation]. Kansas State University; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2097/39823.

Council of Science Editors:

Luo J. Threshold clustering for massive data. [Doctoral Dissertation]. Kansas State University; 2019. Available from: http://hdl.handle.net/2097/39823


NSYSU

15. Tai, Chiech-an. An Automatic Data Clustering Algorithm based on Differential Evolution.

Degree: Master, Computer Science and Engineering, 2013, NSYSU

 As one of the traditional optimization problems, clustering still plays a vital role for the re-searches both theoretically and practically nowadays. Although many successful clustering(more)

Subjects/Keywords: automatic clustering; data clustering; high-dimensional dataset; histogram analysis; differential evolution

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Tai, C. (2013). An Automatic Data Clustering Algorithm based on Differential Evolution. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814

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):

Tai, Chiech-an. “An Automatic Data Clustering Algorithm based on Differential Evolution.” 2013. Thesis, NSYSU. Accessed September 28, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814.

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

MLA Handbook (7th Edition):

Tai, Chiech-an. “An Automatic Data Clustering Algorithm based on Differential Evolution.” 2013. Web. 28 Sep 2020.

Vancouver:

Tai C. An Automatic Data Clustering Algorithm based on Differential Evolution. [Internet] [Thesis]. NSYSU; 2013. [cited 2020 Sep 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814.

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

Council of Science Editors:

Tai C. An Automatic Data Clustering Algorithm based on Differential Evolution. [Thesis]. NSYSU; 2013. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0730113-152814

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


University of Waterloo

16. Kushagra, Shrinu. Theoretical foundations for efficient clustering.

Degree: 2019, University of Waterloo

Clustering aims to group together data instances which are similar while simultaneously separating the dissimilar instances. The task of clustering is challenging due to many… (more)

Subjects/Keywords: Clustering; Same-cluster queries; Data De-duplication; Correlation clustering; Noise-robust clustering; Sparse noise; Regularized clustering; Semi-definite program

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Kushagra, S. (2019). Theoretical foundations for efficient clustering. (Thesis). University of Waterloo. Retrieved from http://hdl.handle.net/10012/14747

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):

Kushagra, Shrinu. “Theoretical foundations for efficient clustering.” 2019. Thesis, University of Waterloo. Accessed September 28, 2020. http://hdl.handle.net/10012/14747.

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

MLA Handbook (7th Edition):

Kushagra, Shrinu. “Theoretical foundations for efficient clustering.” 2019. Web. 28 Sep 2020.

Vancouver:

Kushagra S. Theoretical foundations for efficient clustering. [Internet] [Thesis]. University of Waterloo; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10012/14747.

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

Council of Science Editors:

Kushagra S. Theoretical foundations for efficient clustering. [Thesis]. University of Waterloo; 2019. Available from: http://hdl.handle.net/10012/14747

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

17. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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


Anna University

18. Puniethaa Prabhu. An Efficient Visual Approach For Automatic Clustering And Validation;.

Degree: Computer Science, 2013, Anna University

Clustering or exploratory data analysis is a widely applied newlineunsupervised technique in the data mining domain The major concern of the newlinedomain is how the… (more)

Subjects/Keywords: algorithms; Clustering; data mining; Indexbased; Validation

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Prabhu, P. (2013). An Efficient Visual Approach For Automatic Clustering And Validation;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/26159

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):

Prabhu, Puniethaa. “An Efficient Visual Approach For Automatic Clustering And Validation;.” 2013. Thesis, Anna University. Accessed September 28, 2020. http://shodhganga.inflibnet.ac.in/handle/10603/26159.

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

MLA Handbook (7th Edition):

Prabhu, Puniethaa. “An Efficient Visual Approach For Automatic Clustering And Validation;.” 2013. Web. 28 Sep 2020.

Vancouver:

Prabhu P. An Efficient Visual Approach For Automatic Clustering And Validation;. [Internet] [Thesis]. Anna University; 2013. [cited 2020 Sep 28]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/26159.

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

Council of Science Editors:

Prabhu P. An Efficient Visual Approach For Automatic Clustering And Validation;. [Thesis]. Anna University; 2013. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/26159

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


Penn State University

19. Mandala, Supreet Reddy. Scalable and robust algorithms for clustering large scale networks.

Degree: 2012, Penn State University

 Several social and technological systems around us can be modeled as a network or a graph. The topology of such networks is known to play… (more)

Subjects/Keywords: Social Networks; Clustering; Optimization; Data Mining

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Mandala, S. R. (2012). Scalable and robust algorithms for clustering large scale networks. (Thesis). Penn State University. Retrieved from https://submit-etda.libraries.psu.edu/catalog/16386

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):

Mandala, Supreet Reddy. “Scalable and robust algorithms for clustering large scale networks.” 2012. Thesis, Penn State University. Accessed September 28, 2020. https://submit-etda.libraries.psu.edu/catalog/16386.

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

MLA Handbook (7th Edition):

Mandala, Supreet Reddy. “Scalable and robust algorithms for clustering large scale networks.” 2012. Web. 28 Sep 2020.

Vancouver:

Mandala SR. Scalable and robust algorithms for clustering large scale networks. [Internet] [Thesis]. Penn State University; 2012. [cited 2020 Sep 28]. Available from: https://submit-etda.libraries.psu.edu/catalog/16386.

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

Council of Science Editors:

Mandala SR. Scalable and robust algorithms for clustering large scale networks. [Thesis]. Penn State University; 2012. Available from: https://submit-etda.libraries.psu.edu/catalog/16386

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


University of Rochester

20. Evans, Katie N.; Love, Tanzy. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.

Degree: PhD, 2014, University of Rochester

 In many disciplines, such as marketing, biology, and bioinformatics, there is an increasing desire to identify distinct subgroups of observations within an observed data set;… (more)

Subjects/Keywords: Mixed-type data; Feature selection; Outliers; Clustering

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Evans, Katie N.; Love, T. (2014). Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. (Doctoral Dissertation). University of Rochester. Retrieved from http://hdl.handle.net/1802/28443

Chicago Manual of Style (16th Edition):

Evans, Katie N.; Love, Tanzy. “Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.” 2014. Doctoral Dissertation, University of Rochester. Accessed September 28, 2020. http://hdl.handle.net/1802/28443.

MLA Handbook (7th Edition):

Evans, Katie N.; Love, Tanzy. “Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection.” 2014. Web. 28 Sep 2020.

Vancouver:

Evans, Katie N.; Love T. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. [Internet] [Doctoral dissertation]. University of Rochester; 2014. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/1802/28443.

Council of Science Editors:

Evans, Katie N.; Love T. Extensions to model-based clustering for mixed-type data : a new model framework, variable selection, and outlier detection. [Doctoral Dissertation]. University of Rochester; 2014. Available from: http://hdl.handle.net/1802/28443


Universidade Nova

21. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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

22. Perrot, Alexandre. La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions.

Degree: Docteur es, Informatique, 2017, Bordeaux

L’augmentation de la quantité de données à visualiser due au phénomène du Big Data entraîne de nouveaux défis pour le domaine de la visualisation d’information.… (more)

Subjects/Keywords: Mégadonnées; Partitionnement; Visualisation; Big Data; Clustering; Visualization

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Perrot, A. (2017). La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions. (Doctoral Dissertation). Bordeaux. Retrieved from http://www.theses.fr/2017BORD0775

Chicago Manual of Style (16th Edition):

Perrot, Alexandre. “La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions.” 2017. Doctoral Dissertation, Bordeaux. Accessed September 28, 2020. http://www.theses.fr/2017BORD0775.

MLA Handbook (7th Edition):

Perrot, Alexandre. “La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions.” 2017. Web. 28 Sep 2020.

Vancouver:

Perrot A. La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions. [Internet] [Doctoral dissertation]. Bordeaux; 2017. [cited 2020 Sep 28]. Available from: http://www.theses.fr/2017BORD0775.

Council of Science Editors:

Perrot A. La visualisation d’information à l’ère du Big Data : résoudre les problèmes de scalabilité par l’abstraction multi-échelle : Information Visualization in the Big Data era : tackling scalability issues using multiscale abstractions. [Doctoral Dissertation]. Bordeaux; 2017. Available from: http://www.theses.fr/2017BORD0775


NSYSU

23. Lin, Shu-Yi. The GDense Algorithm for Clustering Data Streams with High Quality.

Degree: Master, Computer Science and Engineering, 2009, NSYSU

 In recent years, mining data streams has been widely studied. A data streams is a sequence of dynamic, continuous, unbounded and real time data items… (more)

Subjects/Keywords: density-based; grid-based; clustering; data streams

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lin, S. (2009). The GDense Algorithm for Clustering Data Streams with High Quality. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0625109-171938

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):

Lin, Shu-Yi. “The GDense Algorithm for Clustering Data Streams with High Quality.” 2009. Thesis, NSYSU. Accessed September 28, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0625109-171938.

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

MLA Handbook (7th Edition):

Lin, Shu-Yi. “The GDense Algorithm for Clustering Data Streams with High Quality.” 2009. Web. 28 Sep 2020.

Vancouver:

Lin S. The GDense Algorithm for Clustering Data Streams with High Quality. [Internet] [Thesis]. NSYSU; 2009. [cited 2020 Sep 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0625109-171938.

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

Council of Science Editors:

Lin S. The GDense Algorithm for Clustering Data Streams with High Quality. [Thesis]. NSYSU; 2009. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0625109-171938

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


NSYSU

24. Chang, Hsi-mei. Hybrid Algorithms of Finding Features for Clustering Sequential Data.

Degree: Master, Computer Science and Engineering, 2010, NSYSU

 Proteins are the structural components of living cells and tissues, and thus an important building block in all living organisms. Patterns in proteins sequences are… (more)

Subjects/Keywords: Clustering; Protein databases; Sequential data; Features

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Chang, H. (2010). Hybrid Algorithms of Finding Features for Clustering Sequential Data. (Thesis). NSYSU. Retrieved from http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708110-144724

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):

Chang, Hsi-mei. “Hybrid Algorithms of Finding Features for Clustering Sequential Data.” 2010. Thesis, NSYSU. Accessed September 28, 2020. http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708110-144724.

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

MLA Handbook (7th Edition):

Chang, Hsi-mei. “Hybrid Algorithms of Finding Features for Clustering Sequential Data.” 2010. Web. 28 Sep 2020.

Vancouver:

Chang H. Hybrid Algorithms of Finding Features for Clustering Sequential Data. [Internet] [Thesis]. NSYSU; 2010. [cited 2020 Sep 28]. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708110-144724.

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

Council of Science Editors:

Chang H. Hybrid Algorithms of Finding Features for Clustering Sequential Data. [Thesis]. NSYSU; 2010. Available from: http://etd.lib.nsysu.edu.tw/ETD-db/ETD-search/view_etd?URN=etd-0708110-144724

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


University of Tennessee – Knoxville

25. Lu, Yuping. Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing.

Degree: 2019, University of Tennessee – Knoxville

 Analysis of what has come to be called “big data” presents a number of challenges as data continues to grow in size, complexity and heterogeneity.… (more)

Subjects/Keywords: big data; robustness; clustering; paraclique; outlier detection

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Lu, Y. (2019). Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing. (Doctoral Dissertation). University of Tennessee – Knoxville. Retrieved from https://trace.tennessee.edu/utk_graddiss/5514

Chicago Manual of Style (16th Edition):

Lu, Yuping. “Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing.” 2019. Doctoral Dissertation, University of Tennessee – Knoxville. Accessed September 28, 2020. https://trace.tennessee.edu/utk_graddiss/5514.

MLA Handbook (7th Edition):

Lu, Yuping. “Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing.” 2019. Web. 28 Sep 2020.

Vancouver:

Lu Y. Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing. [Internet] [Doctoral dissertation]. University of Tennessee – Knoxville; 2019. [cited 2020 Sep 28]. Available from: https://trace.tennessee.edu/utk_graddiss/5514.

Council of Science Editors:

Lu Y. Advances in Big Data Analytics: Algorithmic Stability and Data Cleansing. [Doctoral Dissertation]. University of Tennessee – Knoxville; 2019. Available from: https://trace.tennessee.edu/utk_graddiss/5514


Virginia Tech

26. Abraham, Sherin Ann. PV Hosting Analysis and Demand Response Selection for handling Modern Grid Edge Capability.

Degree: MS, Electrical Engineering, 2019, Virginia Tech

 Today, with significant technological advancements, as we proceed towards a modern grid, a mere change in physical infrastructure will not be enough. With the changes… (more)

Subjects/Keywords: Smart grid; AMI data clustering; PV hosting

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Abraham, S. A. (2019). PV Hosting Analysis and Demand Response Selection for handling Modern Grid Edge Capability. (Masters Thesis). Virginia Tech. Retrieved from http://hdl.handle.net/10919/90774

Chicago Manual of Style (16th Edition):

Abraham, Sherin Ann. “PV Hosting Analysis and Demand Response Selection for handling Modern Grid Edge Capability.” 2019. Masters Thesis, Virginia Tech. Accessed September 28, 2020. http://hdl.handle.net/10919/90774.

MLA Handbook (7th Edition):

Abraham, Sherin Ann. “PV Hosting Analysis and Demand Response Selection for handling Modern Grid Edge Capability.” 2019. Web. 28 Sep 2020.

Vancouver:

Abraham SA. PV Hosting Analysis and Demand Response Selection for handling Modern Grid Edge Capability. [Internet] [Masters thesis]. Virginia Tech; 2019. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/10919/90774.

Council of Science Editors:

Abraham SA. PV Hosting Analysis and Demand Response Selection for handling Modern Grid Edge Capability. [Masters Thesis]. Virginia Tech; 2019. Available from: http://hdl.handle.net/10919/90774


Mid Sweden University

27. Li, Chuhe. A sliding window BIRCH algorithm with performance evaluations.

Degree: Information Systems and Technology, 2017, Mid Sweden University

  An increasing number of applications covered various fields generate transactional data or other time-stamped data which all belongs to time series data. Time series… (more)

Subjects/Keywords: Clustering; time series data; Computer Systems; Datorsystem

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Li, C. (2017). A sliding window BIRCH algorithm with performance evaluations. (Thesis). Mid Sweden University. Retrieved from http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-32397

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):

Li, Chuhe. “A sliding window BIRCH algorithm with performance evaluations.” 2017. Thesis, Mid Sweden University. Accessed September 28, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-32397.

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

MLA Handbook (7th Edition):

Li, Chuhe. “A sliding window BIRCH algorithm with performance evaluations.” 2017. Web. 28 Sep 2020.

Vancouver:

Li C. A sliding window BIRCH algorithm with performance evaluations. [Internet] [Thesis]. Mid Sweden University; 2017. [cited 2020 Sep 28]. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-32397.

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

Council of Science Editors:

Li C. A sliding window BIRCH algorithm with performance evaluations. [Thesis]. Mid Sweden University; 2017. Available from: http://urn.kb.se/resolve?urn=urn:nbn:se:miun:diva-32397

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


University of Toronto

28. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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 Illinois – Urbana-Champaign

29. Zhu, Xiaolu. Heterogeneity modeling and longitudinal clustering.

Degree: PhD, Statistics, 2017, University of Illinois – Urbana-Champaign

 Personalization has broad applications in many fields these days. Due to significant subject variations, it has become critical to incorporate subjects' heterogeneous characteristics in order… (more)

Subjects/Keywords: Clustering; Heterogeneity modeling; Longitudinal data; Subgrouping

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

APA (6th Edition):

Zhu, X. (2017). Heterogeneity modeling and longitudinal clustering. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/98160

Chicago Manual of Style (16th Edition):

Zhu, Xiaolu. “Heterogeneity modeling and longitudinal clustering.” 2017. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed September 28, 2020. http://hdl.handle.net/2142/98160.

MLA Handbook (7th Edition):

Zhu, Xiaolu. “Heterogeneity modeling and longitudinal clustering.” 2017. Web. 28 Sep 2020.

Vancouver:

Zhu X. Heterogeneity modeling and longitudinal clustering. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2017. [cited 2020 Sep 28]. Available from: http://hdl.handle.net/2142/98160.

Council of Science Editors:

Zhu X. Heterogeneity modeling and longitudinal clustering. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2017. Available from: http://hdl.handle.net/2142/98160


University of Minnesota

30. 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

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

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

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

[1] [2] [3] [4] [5] … [25]

.