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You searched for subject:(graph data mining). Showing records 1 – 30 of 98 total matches.

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Anna University

1. Sabeen S. Association rule mining using directed graphs and hypergraphs;.

Degree: Association rule mining using directed graphs and hypergraphs, 2015, Anna University

Data mining is a potential tool for prolific analysis of data The task of association rule mining in a large database is one of the… (more)

Subjects/Keywords: Data mining; Directed graph model

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APA (6th Edition):

S, S. (2015). Association rule mining using directed graphs and hypergraphs;. (Thesis). Anna University. Retrieved from http://shodhganga.inflibnet.ac.in/handle/10603/40129

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):

S, Sabeen. “Association rule mining using directed graphs and hypergraphs;.” 2015. Thesis, Anna University. Accessed December 15, 2019. http://shodhganga.inflibnet.ac.in/handle/10603/40129.

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

MLA Handbook (7th Edition):

S, Sabeen. “Association rule mining using directed graphs and hypergraphs;.” 2015. Web. 15 Dec 2019.

Vancouver:

S S. Association rule mining using directed graphs and hypergraphs;. [Internet] [Thesis]. Anna University; 2015. [cited 2019 Dec 15]. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40129.

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

Council of Science Editors:

S S. Association rule mining using directed graphs and hypergraphs;. [Thesis]. Anna University; 2015. Available from: http://shodhganga.inflibnet.ac.in/handle/10603/40129

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


University of Hong Kong

2. Fang, Yixiang. Effective and efficient community search over large attributed graphs.

Degree: PhD, 2017, University of Hong Kong

 Communities, which are prevalent in attributed graphs such as social networks and knowledge bases, can be used in emerging applications such as product advertisement and… (more)

Subjects/Keywords: Data mining; Graph algorithms

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APA (6th Edition):

Fang, Y. (2017). Effective and efficient community search over large attributed graphs. (Doctoral Dissertation). University of Hong Kong. Retrieved from http://hdl.handle.net/10722/250721

Chicago Manual of Style (16th Edition):

Fang, Yixiang. “Effective and efficient community search over large attributed graphs.” 2017. Doctoral Dissertation, University of Hong Kong. Accessed December 15, 2019. http://hdl.handle.net/10722/250721.

MLA Handbook (7th Edition):

Fang, Yixiang. “Effective and efficient community search over large attributed graphs.” 2017. Web. 15 Dec 2019.

Vancouver:

Fang Y. Effective and efficient community search over large attributed graphs. [Internet] [Doctoral dissertation]. University of Hong Kong; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10722/250721.

Council of Science Editors:

Fang Y. Effective and efficient community search over large attributed graphs. [Doctoral Dissertation]. University of Hong Kong; 2017. Available from: http://hdl.handle.net/10722/250721


Louisiana State University

3. Jose, Neha Clare. Social Media Network Data Mining and Optimization.

Degree: MS, Computer Sciences, 2016, Louisiana State University

 Many small social aid organizations could benefit from collaborating with other organizations on common causes, but may not have the necessary social relationships. We present… (more)

Subjects/Keywords: Graph Analysis; Centrality; Data Mining

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APA (6th Edition):

Jose, N. C. (2016). Social Media Network Data Mining and Optimization. (Masters Thesis). Louisiana State University. Retrieved from etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024

Chicago Manual of Style (16th Edition):

Jose, Neha Clare. “Social Media Network Data Mining and Optimization.” 2016. Masters Thesis, Louisiana State University. Accessed December 15, 2019. etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024.

MLA Handbook (7th Edition):

Jose, Neha Clare. “Social Media Network Data Mining and Optimization.” 2016. Web. 15 Dec 2019.

Vancouver:

Jose NC. Social Media Network Data Mining and Optimization. [Internet] [Masters thesis]. Louisiana State University; 2016. [cited 2019 Dec 15]. Available from: etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024.

Council of Science Editors:

Jose NC. Social Media Network Data Mining and Optimization. [Masters Thesis]. Louisiana State University; 2016. Available from: etd-05252016-161222 ; https://digitalcommons.lsu.edu/gradschool_theses/3024

4. Zhang, Yao. Optimizing and Understanding Network Structure for Diffusion.

Degree: PhD, Computer Science, 2017, Virginia Tech

 Given a population contact network and electronic medical records of patients, how to distribute vaccines to individuals to effectively control a flu epidemic? Similarly, given… (more)

Subjects/Keywords: Data Mining; Graph/Network; Diffusion

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APA (6th Edition):

Zhang, Y. (2017). Optimizing and Understanding Network Structure for Diffusion. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/79674

Chicago Manual of Style (16th Edition):

Zhang, Yao. “Optimizing and Understanding Network Structure for Diffusion.” 2017. Doctoral Dissertation, Virginia Tech. Accessed December 15, 2019. http://hdl.handle.net/10919/79674.

MLA Handbook (7th Edition):

Zhang, Yao. “Optimizing and Understanding Network Structure for Diffusion.” 2017. Web. 15 Dec 2019.

Vancouver:

Zhang Y. Optimizing and Understanding Network Structure for Diffusion. [Internet] [Doctoral dissertation]. Virginia Tech; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10919/79674.

Council of Science Editors:

Zhang Y. Optimizing and Understanding Network Structure for Diffusion. [Doctoral Dissertation]. Virginia Tech; 2017. Available from: http://hdl.handle.net/10919/79674


Rutgers University

5. Kang, Yunyi, 1991-. Anomaly detection in network using non-negative matrix factorization techniques.

Degree: MS, Industrial and Systems Engineering, 2015, Rutgers University

 Anomaly detection is becoming an important problem in graph mining. This is because people are eager to find out unusual objects or patterns in a… (more)

Subjects/Keywords: Graph theory; Anomalies; Data mining

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

APA (6th Edition):

Kang, Yunyi, 1. (2015). Anomaly detection in network using non-negative matrix factorization techniques. (Masters Thesis). Rutgers University. Retrieved from https://rucore.libraries.rutgers.edu/rutgers-lib/48536/

Chicago Manual of Style (16th Edition):

Kang, Yunyi, 1991-. “Anomaly detection in network using non-negative matrix factorization techniques.” 2015. Masters Thesis, Rutgers University. Accessed December 15, 2019. https://rucore.libraries.rutgers.edu/rutgers-lib/48536/.

MLA Handbook (7th Edition):

Kang, Yunyi, 1991-. “Anomaly detection in network using non-negative matrix factorization techniques.” 2015. Web. 15 Dec 2019.

Vancouver:

Kang, Yunyi 1. Anomaly detection in network using non-negative matrix factorization techniques. [Internet] [Masters thesis]. Rutgers University; 2015. [cited 2019 Dec 15]. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48536/.

Council of Science Editors:

Kang, Yunyi 1. Anomaly detection in network using non-negative matrix factorization techniques. [Masters Thesis]. Rutgers University; 2015. Available from: https://rucore.libraries.rutgers.edu/rutgers-lib/48536/


Université Catholique de Louvain

6. Demesmaeker, Florian. Graph cube mining.

Degree: 2017, Université Catholique de Louvain

Due to the availability of rich network data, graph mining techniques have been improved to handle the emergence of such heterogeneous data. Exploratory data analysis… (more)

Subjects/Keywords: Pattern mining; Hypothesis testing; Graph mining; Graph cube; Data mining

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

APA (6th Edition):

Demesmaeker, F. (2017). Graph cube mining. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/thesis:10691

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):

Demesmaeker, Florian. “Graph cube mining.” 2017. Thesis, Université Catholique de Louvain. Accessed December 15, 2019. http://hdl.handle.net/2078.1/thesis:10691.

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

MLA Handbook (7th Edition):

Demesmaeker, Florian. “Graph cube mining.” 2017. Web. 15 Dec 2019.

Vancouver:

Demesmaeker F. Graph cube mining. [Internet] [Thesis]. Université Catholique de Louvain; 2017. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2078.1/thesis:10691.

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

Council of Science Editors:

Demesmaeker F. Graph cube mining. [Thesis]. Université Catholique de Louvain; 2017. Available from: http://hdl.handle.net/2078.1/thesis:10691

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


Hong Kong University of Science and Technology

7. Wang, Changliang. Continuous subgraph pattern search over graph streams.

Degree: 2009, Hong Kong University of Science and Technology

 Search over graph databases has attracted much attention recently due to its usefulness in many fields, such as the analysis of chemical compounds, intrusion detection… (more)

Subjects/Keywords: Data mining; Graph theory  – Data processing

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

APA (6th Edition):

Wang, C. (2009). Continuous subgraph pattern search over graph streams. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-b1054338 ; http://repository.ust.hk/ir/bitstream/1783.1-6071/1/th_redirect.html

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, Changliang. “Continuous subgraph pattern search over graph streams.” 2009. Thesis, Hong Kong University of Science and Technology. Accessed December 15, 2019. https://doi.org/10.14711/thesis-b1054338 ; http://repository.ust.hk/ir/bitstream/1783.1-6071/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Wang, Changliang. “Continuous subgraph pattern search over graph streams.” 2009. Web. 15 Dec 2019.

Vancouver:

Wang C. Continuous subgraph pattern search over graph streams. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2009. [cited 2019 Dec 15]. Available from: https://doi.org/10.14711/thesis-b1054338 ; http://repository.ust.hk/ir/bitstream/1783.1-6071/1/th_redirect.html.

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

Council of Science Editors:

Wang C. Continuous subgraph pattern search over graph streams. [Thesis]. Hong Kong University of Science and Technology; 2009. Available from: https://doi.org/10.14711/thesis-b1054338 ; http://repository.ust.hk/ir/bitstream/1783.1-6071/1/th_redirect.html

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


IUPUI

8. Mandal, Aritra. Distributed graph decomposition algorithms on Apache Spark.

Degree: 2018, IUPUI

Indiana University-Purdue University Indianapolis (IUPUI)

Structural analysis and mining of large and complex graphs for describing the characteristics of a vertex or an edge in… (more)

Subjects/Keywords: Graph Mining; Big Data; Apache Spark; Graph Decomposition; Graph Partitioning; Clustering

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

APA (6th Edition):

Mandal, A. (2018). Distributed graph decomposition algorithms on Apache Spark. (Thesis). IUPUI. Retrieved from http://hdl.handle.net/1805/16924

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):

Mandal, Aritra. “Distributed graph decomposition algorithms on Apache Spark.” 2018. Thesis, IUPUI. Accessed December 15, 2019. http://hdl.handle.net/1805/16924.

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

MLA Handbook (7th Edition):

Mandal, Aritra. “Distributed graph decomposition algorithms on Apache Spark.” 2018. Web. 15 Dec 2019.

Vancouver:

Mandal A. Distributed graph decomposition algorithms on Apache Spark. [Internet] [Thesis]. IUPUI; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1805/16924.

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

Council of Science Editors:

Mandal A. Distributed graph decomposition algorithms on Apache Spark. [Thesis]. IUPUI; 2018. Available from: http://hdl.handle.net/1805/16924

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


Queensland University of Technology

9. Hassanzadeh, Reza. Anomaly detection in online social networks : using data-mining techniques and fuzzy logic.

Degree: 2014, Queensland University of Technology

 This research is a step forward in improving the accuracy of detecting anomaly in a data graph representing connectivity between people in an online social… (more)

Subjects/Keywords: Anomaly Detection; Fuzzy Logig; Data Mining; Data Graph; Graph Theory

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

APA (6th Edition):

Hassanzadeh, R. (2014). Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. (Thesis). Queensland University of Technology. Retrieved from https://eprints.qut.edu.au/78679/

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):

Hassanzadeh, Reza. “Anomaly detection in online social networks : using data-mining techniques and fuzzy logic.” 2014. Thesis, Queensland University of Technology. Accessed December 15, 2019. https://eprints.qut.edu.au/78679/.

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

MLA Handbook (7th Edition):

Hassanzadeh, Reza. “Anomaly detection in online social networks : using data-mining techniques and fuzzy logic.” 2014. Web. 15 Dec 2019.

Vancouver:

Hassanzadeh R. Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. [Internet] [Thesis]. Queensland University of Technology; 2014. [cited 2019 Dec 15]. Available from: https://eprints.qut.edu.au/78679/.

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

Council of Science Editors:

Hassanzadeh R. Anomaly detection in online social networks : using data-mining techniques and fuzzy logic. [Thesis]. Queensland University of Technology; 2014. Available from: https://eprints.qut.edu.au/78679/

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


University of Alberta

10. Simoes Gomes, Carolina. Heavyweight Pattern Mining in Attributed Flow Graphs.

Degree: MS, Department of Computing Science, 2012, University of Alberta

 Flow graphs are an abstraction used to represent elements travelling through a network of nodes. The paths between nodes are directed edges in the graph,… (more)

Subjects/Keywords: software analysis; pattern mining; program analysis; flow graph; program profiling; sub-graph mining; data mining

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APA (6th Edition):

Simoes Gomes, C. (2012). Heavyweight Pattern Mining in Attributed Flow Graphs. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/r494vk17h

Chicago Manual of Style (16th Edition):

Simoes Gomes, Carolina. “Heavyweight Pattern Mining in Attributed Flow Graphs.” 2012. Masters Thesis, University of Alberta. Accessed December 15, 2019. https://era.library.ualberta.ca/files/r494vk17h.

MLA Handbook (7th Edition):

Simoes Gomes, Carolina. “Heavyweight Pattern Mining in Attributed Flow Graphs.” 2012. Web. 15 Dec 2019.

Vancouver:

Simoes Gomes C. Heavyweight Pattern Mining in Attributed Flow Graphs. [Internet] [Masters thesis]. University of Alberta; 2012. [cited 2019 Dec 15]. Available from: https://era.library.ualberta.ca/files/r494vk17h.

Council of Science Editors:

Simoes Gomes C. Heavyweight Pattern Mining in Attributed Flow Graphs. [Masters Thesis]. University of Alberta; 2012. Available from: https://era.library.ualberta.ca/files/r494vk17h


Iowa State University

11. Mousavi Hanjani, Kiana. Improved triangle counting in graph streams: Neighborhood multi-sampling.

Degree: 2018, Iowa State University

 In this thesis, we study the problem of estimating the number of triangles of an undirected graph in the data stream model. Some of the… (more)

Subjects/Keywords: big data; data mining; graph mining; streaming algorithms; Computer Sciences

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APA (6th Edition):

Mousavi Hanjani, K. (2018). Improved triangle counting in graph streams: Neighborhood multi-sampling. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/16644

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):

Mousavi Hanjani, Kiana. “Improved triangle counting in graph streams: Neighborhood multi-sampling.” 2018. Thesis, Iowa State University. Accessed December 15, 2019. https://lib.dr.iastate.edu/etd/16644.

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

MLA Handbook (7th Edition):

Mousavi Hanjani, Kiana. “Improved triangle counting in graph streams: Neighborhood multi-sampling.” 2018. Web. 15 Dec 2019.

Vancouver:

Mousavi Hanjani K. Improved triangle counting in graph streams: Neighborhood multi-sampling. [Internet] [Thesis]. Iowa State University; 2018. [cited 2019 Dec 15]. Available from: https://lib.dr.iastate.edu/etd/16644.

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

Council of Science Editors:

Mousavi Hanjani K. Improved triangle counting in graph streams: Neighborhood multi-sampling. [Thesis]. Iowa State University; 2018. Available from: https://lib.dr.iastate.edu/etd/16644

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


Louisiana State University

12. Shakya, Shobhit Sandesh. Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data.

Degree: PhD, Computer Sciences, 2015, Louisiana State University

 Molecular dynamics simulation method is widely used to calculate and understand a wide range of properties of materials. A lot of research efforts have been… (more)

Subjects/Keywords: data mining; sub-graph mining; close path mining; molecular dynamics; pattern mining

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APA (6th Edition):

Shakya, S. S. (2015). Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. (Doctoral Dissertation). Louisiana State University. Retrieved from etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034

Chicago Manual of Style (16th Edition):

Shakya, Shobhit Sandesh. “Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data.” 2015. Doctoral Dissertation, Louisiana State University. Accessed December 15, 2019. etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034.

MLA Handbook (7th Edition):

Shakya, Shobhit Sandesh. “Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data.” 2015. Web. 15 Dec 2019.

Vancouver:

Shakya SS. Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. [Internet] [Doctoral dissertation]. Louisiana State University; 2015. [cited 2019 Dec 15]. Available from: etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034.

Council of Science Editors:

Shakya SS. Pattern Mining and Events Discovery in Molecular Dynamics Simulations Data. [Doctoral Dissertation]. Louisiana State University; 2015. Available from: etd-04092015-224028 ; https://digitalcommons.lsu.edu/gradschool_dissertations/2034


Penn State University

13. Chatterjee, Anirban. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.

Degree: PhD, Computer Science and Engineering, 2011, Penn State University

Data-driven discovery seeks to obtain a computational model of the underlying process using observed data on a large number of variables. Observations can be viewed… (more)

Subjects/Keywords: sparse graph embedding; sparse graph partitioning; data mining; sparse linear solvers

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

APA (6th Edition):

Chatterjee, A. (2011). Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/12026

Chicago Manual of Style (16th Edition):

Chatterjee, Anirban. “Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.” 2011. Doctoral Dissertation, Penn State University. Accessed December 15, 2019. https://etda.libraries.psu.edu/catalog/12026.

MLA Handbook (7th Edition):

Chatterjee, Anirban. “Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery.” 2011. Web. 15 Dec 2019.

Vancouver:

Chatterjee A. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. [Internet] [Doctoral dissertation]. Penn State University; 2011. [cited 2019 Dec 15]. Available from: https://etda.libraries.psu.edu/catalog/12026.

Council of Science Editors:

Chatterjee A. Exploiting Sparsity, Structure, and Geometry for Knowledge Discovery. [Doctoral Dissertation]. Penn State University; 2011. Available from: https://etda.libraries.psu.edu/catalog/12026


Indian Institute of Science

14. Ranga Suri, N N R. Outlier Detection with Applications in Graph Data Mining.

Degree: 2013, Indian Institute of Science

 Outlier detection is an important data mining task due to its applicability in many contemporary applications such as fraud detection and anomaly detection in networks,… (more)

Subjects/Keywords: Data Mining; Graph Data Mining; Outlier Detection; Categorical Data - Outlier Detection; Network/Graph Data - Outlier Detection; Graph Data Mining - Outlier Detection; Outliers; Rough Clustering Algorithm; Computer Science

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APA (6th Edition):

Ranga Suri, N. N. R. (2013). Outlier Detection with Applications in Graph Data Mining. (Thesis). Indian Institute of Science. Retrieved from http://etd.iisc.ernet.in/2005/3447 ; http://etd.iisc.ernet.in/abstracts/4314/G25969-Abs.pdf

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):

Ranga Suri, N N R. “Outlier Detection with Applications in Graph Data Mining.” 2013. Thesis, Indian Institute of Science. Accessed December 15, 2019. http://etd.iisc.ernet.in/2005/3447 ; http://etd.iisc.ernet.in/abstracts/4314/G25969-Abs.pdf.

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

MLA Handbook (7th Edition):

Ranga Suri, N N R. “Outlier Detection with Applications in Graph Data Mining.” 2013. Web. 15 Dec 2019.

Vancouver:

Ranga Suri NNR. Outlier Detection with Applications in Graph Data Mining. [Internet] [Thesis]. Indian Institute of Science; 2013. [cited 2019 Dec 15]. Available from: http://etd.iisc.ernet.in/2005/3447 ; http://etd.iisc.ernet.in/abstracts/4314/G25969-Abs.pdf.

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

Council of Science Editors:

Ranga Suri NNR. Outlier Detection with Applications in Graph Data Mining. [Thesis]. Indian Institute of Science; 2013. Available from: http://etd.iisc.ernet.in/2005/3447 ; http://etd.iisc.ernet.in/abstracts/4314/G25969-Abs.pdf

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


Virginia Tech

15. Cadena, Jose Eduardo. Finding Interesting Subgraphs with Guarantees.

Degree: PhD, Computer Science, 2018, Virginia Tech

 Networks are a mathematical abstraction of the interactions between a set of entities, with extensive applications in social science, epidemiology, bioinformatics, and cybersecurity, among others.… (more)

Subjects/Keywords: Graph Mining; Data Mining; Graph Algorithms; Anomaly Detection; Finding Subgraphs; Parameterized Complexity; Distributed Algorithms

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

APA (6th Edition):

Cadena, J. E. (2018). Finding Interesting Subgraphs with Guarantees. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/81960

Chicago Manual of Style (16th Edition):

Cadena, Jose Eduardo. “Finding Interesting Subgraphs with Guarantees.” 2018. Doctoral Dissertation, Virginia Tech. Accessed December 15, 2019. http://hdl.handle.net/10919/81960.

MLA Handbook (7th Edition):

Cadena, Jose Eduardo. “Finding Interesting Subgraphs with Guarantees.” 2018. Web. 15 Dec 2019.

Vancouver:

Cadena JE. Finding Interesting Subgraphs with Guarantees. [Internet] [Doctoral dissertation]. Virginia Tech; 2018. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10919/81960.

Council of Science Editors:

Cadena JE. Finding Interesting Subgraphs with Guarantees. [Doctoral Dissertation]. Virginia Tech; 2018. Available from: http://hdl.handle.net/10919/81960


Iowa State University

16. Wang, Heyong. A two-stage strategy for solving the connection subgraph problem.

Degree: 2012, Iowa State University

 A connection subgraph is a small subgraph of a large graph that best capture the relationship between two nodes. Formally, Connection Subgraph Problem is: Given:… (more)

Subjects/Keywords: Connection Subgraph; Data Mining; Graph Betweenness; Graph Mining; Path Between; Computer Sciences

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

APA (6th Edition):

Wang, H. (2012). A two-stage strategy for solving the connection subgraph problem. (Thesis). Iowa State University. Retrieved from https://lib.dr.iastate.edu/etd/12507

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, Heyong. “A two-stage strategy for solving the connection subgraph problem.” 2012. Thesis, Iowa State University. Accessed December 15, 2019. https://lib.dr.iastate.edu/etd/12507.

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

MLA Handbook (7th Edition):

Wang, Heyong. “A two-stage strategy for solving the connection subgraph problem.” 2012. Web. 15 Dec 2019.

Vancouver:

Wang H. A two-stage strategy for solving the connection subgraph problem. [Internet] [Thesis]. Iowa State University; 2012. [cited 2019 Dec 15]. Available from: https://lib.dr.iastate.edu/etd/12507.

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

Council of Science Editors:

Wang H. A two-stage strategy for solving the connection subgraph problem. [Thesis]. Iowa State University; 2012. Available from: https://lib.dr.iastate.edu/etd/12507

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


Texas A&M University

17. Sangelkar, Shraddha Chandrakant. Automated Inclusive Design Heuristics Generation with Graph Mining.

Degree: 2013, Texas A&M University

 Inclusive design is a concept intended to promote the development of products and environments equally usable by all users, irrespective of their age or ability.… (more)

Subjects/Keywords: Inclusive Design; Graph Mining; Data Mining; User Centric Design; Universal Design

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

APA (6th Edition):

Sangelkar, S. C. (2013). Automated Inclusive Design Heuristics Generation with Graph Mining. (Thesis). Texas A&M University. Retrieved from http://hdl.handle.net/1969.1/151329

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):

Sangelkar, Shraddha Chandrakant. “Automated Inclusive Design Heuristics Generation with Graph Mining.” 2013. Thesis, Texas A&M University. Accessed December 15, 2019. http://hdl.handle.net/1969.1/151329.

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

MLA Handbook (7th Edition):

Sangelkar, Shraddha Chandrakant. “Automated Inclusive Design Heuristics Generation with Graph Mining.” 2013. Web. 15 Dec 2019.

Vancouver:

Sangelkar SC. Automated Inclusive Design Heuristics Generation with Graph Mining. [Internet] [Thesis]. Texas A&M University; 2013. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1969.1/151329.

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

Council of Science Editors:

Sangelkar SC. Automated Inclusive Design Heuristics Generation with Graph Mining. [Thesis]. Texas A&M University; 2013. Available from: http://hdl.handle.net/1969.1/151329

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


Case Western Reserve University

18. Cederquist, Aaron. Frequent Pattern Mining among Weighted and Directed Graphs.

Degree: MSs, EECS - Computer and Information Sciences, 2009, Case Western Reserve University

Mining frequent graph patterns has great practical implications, since data in numerous application domains such as biology, sociology, and finance, can be represented as graphs.… (more)

Subjects/Keywords: Computer Science; data mining; graph; canonical form; frequent pattern mining

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

APA (6th Edition):

Cederquist, A. (2009). Frequent Pattern Mining among Weighted and Directed Graphs. (Masters Thesis). Case Western Reserve University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123

Chicago Manual of Style (16th Edition):

Cederquist, Aaron. “Frequent Pattern Mining among Weighted and Directed Graphs.” 2009. Masters Thesis, Case Western Reserve University. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123.

MLA Handbook (7th Edition):

Cederquist, Aaron. “Frequent Pattern Mining among Weighted and Directed Graphs.” 2009. Web. 15 Dec 2019.

Vancouver:

Cederquist A. Frequent Pattern Mining among Weighted and Directed Graphs. [Internet] [Masters thesis]. Case Western Reserve University; 2009. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123.

Council of Science Editors:

Cederquist A. Frequent Pattern Mining among Weighted and Directed Graphs. [Masters Thesis]. Case Western Reserve University; 2009. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=case1228328123


University of Oregon

19. Liu, Haishan. A Graph-based Approach for Semantic Data Mining.

Degree: 2012, University of Oregon

Data mining is the nontrivial extraction of implicit, previously unknown, and potentially useful information from data. It is widely acknowledged that the role of domain… (more)

Subjects/Keywords: domain knowledge; graph mining; ontology; semantic data mining

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

APA (6th Edition):

Liu, H. (2012). A Graph-based Approach for Semantic Data Mining. (Thesis). University of Oregon. Retrieved from http://hdl.handle.net/1794/12567

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

Chicago Manual of Style (16th Edition):

Liu, Haishan. “A Graph-based Approach for Semantic Data Mining.” 2012. Thesis, University of Oregon. Accessed December 15, 2019. http://hdl.handle.net/1794/12567.

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

MLA Handbook (7th Edition):

Liu, Haishan. “A Graph-based Approach for Semantic Data Mining.” 2012. Web. 15 Dec 2019.

Vancouver:

Liu H. A Graph-based Approach for Semantic Data Mining. [Internet] [Thesis]. University of Oregon; 2012. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/1794/12567.

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

Council of Science Editors:

Liu H. A Graph-based Approach for Semantic Data Mining. [Thesis]. University of Oregon; 2012. Available from: http://hdl.handle.net/1794/12567

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


Université Catholique de Louvain

20. Senelle, Mathieu. Measures on graphs : from similarity to density.

Degree: 2014, Université Catholique de Louvain

In this thesis, we mainly consider two closely related classical and general problems in data mining and machine learning: (1) developing new similarity measures between… (more)

Subjects/Keywords: Data Mining; Graph Mining; Classification; Clustering; Matrix-Forest Theorem

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

APA (6th Edition):

Senelle, M. (2014). Measures on graphs : from similarity to density. (Thesis). Université Catholique de Louvain. Retrieved from http://hdl.handle.net/2078.1/161671

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):

Senelle, Mathieu. “Measures on graphs : from similarity to density.” 2014. Thesis, Université Catholique de Louvain. Accessed December 15, 2019. http://hdl.handle.net/2078.1/161671.

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

MLA Handbook (7th Edition):

Senelle, Mathieu. “Measures on graphs : from similarity to density.” 2014. Web. 15 Dec 2019.

Vancouver:

Senelle M. Measures on graphs : from similarity to density. [Internet] [Thesis]. Université Catholique de Louvain; 2014. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2078.1/161671.

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

Council of Science Editors:

Senelle M. Measures on graphs : from similarity to density. [Thesis]. Université Catholique de Louvain; 2014. Available from: http://hdl.handle.net/2078.1/161671

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


Purdue University

21. Ahmed, Nesreen Kamel. Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery.

Degree: PhD, Computer Science, 2015, Purdue University

 Network analysis and graph mining play a prominent role in providing insights and studying phenomena across various domains, including social, behavioral, biological, transportation, communication, and… (more)

Subjects/Keywords: Data Mining; Graph Mining; Machine Learning; Network Science; Sampling; Statistical Estimation

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

APA (6th Edition):

Ahmed, N. K. (2015). Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. (Doctoral Dissertation). Purdue University. Retrieved from https://docs.lib.purdue.edu/open_access_dissertations/1445

Chicago Manual of Style (16th Edition):

Ahmed, Nesreen Kamel. “Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery.” 2015. Doctoral Dissertation, Purdue University. Accessed December 15, 2019. https://docs.lib.purdue.edu/open_access_dissertations/1445.

MLA Handbook (7th Edition):

Ahmed, Nesreen Kamel. “Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery.” 2015. Web. 15 Dec 2019.

Vancouver:

Ahmed NK. Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. [Internet] [Doctoral dissertation]. Purdue University; 2015. [cited 2019 Dec 15]. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1445.

Council of Science Editors:

Ahmed NK. Scaling Up Network Analysis and Mining: Statistical Sampling, Estimation, and Pattern Discovery. [Doctoral Dissertation]. Purdue University; 2015. Available from: https://docs.lib.purdue.edu/open_access_dissertations/1445


Hong Kong University of Science and Technology

22. Wu, Yanhong CSE. On the scalability of large graph visualization.

Degree: 2017, Hong Kong University of Science and Technology

 As a natural representation of data, graph structures exist in many domains such as finance, sociology, biology, and software engineering. Visualization techniques have been widely… (more)

Subjects/Keywords: Information visualization; Data processing; Graph theory; Data mining; Visual analytics

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

APA (6th Edition):

Wu, Y. C. (2017). On the scalability of large graph visualization. (Thesis). Hong Kong University of Science and Technology. Retrieved from https://doi.org/10.14711/thesis-991012553967403412 ; http://repository.ust.hk/ir/bitstream/1783.1-95163/1/th_redirect.html

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):

Wu, Yanhong CSE. “On the scalability of large graph visualization.” 2017. Thesis, Hong Kong University of Science and Technology. Accessed December 15, 2019. https://doi.org/10.14711/thesis-991012553967403412 ; http://repository.ust.hk/ir/bitstream/1783.1-95163/1/th_redirect.html.

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

MLA Handbook (7th Edition):

Wu, Yanhong CSE. “On the scalability of large graph visualization.” 2017. Web. 15 Dec 2019.

Vancouver:

Wu YC. On the scalability of large graph visualization. [Internet] [Thesis]. Hong Kong University of Science and Technology; 2017. [cited 2019 Dec 15]. Available from: https://doi.org/10.14711/thesis-991012553967403412 ; http://repository.ust.hk/ir/bitstream/1783.1-95163/1/th_redirect.html.

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

Council of Science Editors:

Wu YC. On the scalability of large graph visualization. [Thesis]. Hong Kong University of Science and Technology; 2017. Available from: https://doi.org/10.14711/thesis-991012553967403412 ; http://repository.ust.hk/ir/bitstream/1783.1-95163/1/th_redirect.html

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


University of Illinois – Urbana-Champaign

23. Huang, Edward W. Integrating heterogeneous data into electronic medical record analysis.

Degree: PhD, Computer Science, 2019, University of Illinois – Urbana-Champaign

 Electronic medical records (EMRs) are the digital equivalent of paper records at a clinician's office. They contain patient information such as treatment and medical history,… (more)

Subjects/Keywords: Electronic medical records; data mining; knowledge graph; heterogeneous data

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

APA (6th Edition):

Huang, E. W. (2019). Integrating heterogeneous data into electronic medical record analysis. (Doctoral Dissertation). University of Illinois – Urbana-Champaign. Retrieved from http://hdl.handle.net/2142/104778

Chicago Manual of Style (16th Edition):

Huang, Edward W. “Integrating heterogeneous data into electronic medical record analysis.” 2019. Doctoral Dissertation, University of Illinois – Urbana-Champaign. Accessed December 15, 2019. http://hdl.handle.net/2142/104778.

MLA Handbook (7th Edition):

Huang, Edward W. “Integrating heterogeneous data into electronic medical record analysis.” 2019. Web. 15 Dec 2019.

Vancouver:

Huang EW. Integrating heterogeneous data into electronic medical record analysis. [Internet] [Doctoral dissertation]. University of Illinois – Urbana-Champaign; 2019. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/2142/104778.

Council of Science Editors:

Huang EW. Integrating heterogeneous data into electronic medical record analysis. [Doctoral Dissertation]. University of Illinois – Urbana-Champaign; 2019. Available from: http://hdl.handle.net/2142/104778


University of Illinois – Chicago

24. Kong, Xiangnan. Modeling Big Data Variety with Graph Mining Techniques.

Degree: 2014, University of Illinois – Chicago

 Graphs are ubiquitous and have become increasingly important in modeling diverse kinds of objects. In many real-world applications, instances are not represented as feature vectors,… (more)

Subjects/Keywords: Graph Mining; Data Mining; Big Data; Data Variety; Subgraph Pattern; Feature Selection; Uncertain Data; Drug Discovery; Brain Network

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APA (6th Edition):

Kong, X. (2014). Modeling Big Data Variety with Graph Mining Techniques. (Thesis). University of Illinois – Chicago. Retrieved from http://hdl.handle.net/10027/19119

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):

Kong, Xiangnan. “Modeling Big Data Variety with Graph Mining Techniques.” 2014. Thesis, University of Illinois – Chicago. Accessed December 15, 2019. http://hdl.handle.net/10027/19119.

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

MLA Handbook (7th Edition):

Kong, Xiangnan. “Modeling Big Data Variety with Graph Mining Techniques.” 2014. Web. 15 Dec 2019.

Vancouver:

Kong X. Modeling Big Data Variety with Graph Mining Techniques. [Internet] [Thesis]. University of Illinois – Chicago; 2014. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10027/19119.

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

Council of Science Editors:

Kong X. Modeling Big Data Variety with Graph Mining Techniques. [Thesis]. University of Illinois – Chicago; 2014. Available from: http://hdl.handle.net/10027/19119

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


University of Cincinnati

25. Fang, Chunsheng. Novel Frameworks for Mining Heterogeneous and Dynamic Networks.

Degree: PhD, Engineering and Applied Science: Computer Science and Engineering, 2011, University of Cincinnati

 Graphs serve as an important tool for discrete data representation. Recently, graph representations have made possible very powerful machine learning algorithms, such as manifold learning,… (more)

Subjects/Keywords: Computer Science; machine learning; social network; data mining; manifold learning; graph embedding; dynamic graph

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

APA (6th Edition):

Fang, C. (2011). Novel Frameworks for Mining Heterogeneous and Dynamic Networks. (Doctoral Dissertation). University of Cincinnati. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978

Chicago Manual of Style (16th Edition):

Fang, Chunsheng. “Novel Frameworks for Mining Heterogeneous and Dynamic Networks.” 2011. Doctoral Dissertation, University of Cincinnati. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.

MLA Handbook (7th Edition):

Fang, Chunsheng. “Novel Frameworks for Mining Heterogeneous and Dynamic Networks.” 2011. Web. 15 Dec 2019.

Vancouver:

Fang C. Novel Frameworks for Mining Heterogeneous and Dynamic Networks. [Internet] [Doctoral dissertation]. University of Cincinnati; 2011. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978.

Council of Science Editors:

Fang C. Novel Frameworks for Mining Heterogeneous and Dynamic Networks. [Doctoral Dissertation]. University of Cincinnati; 2011. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=ucin1321369978


University of Georgia

26. Patch, Jerrad Arthur. Detecting bullying on Twitter using emotion lexicons.

Degree: MS, Computer Science, 2015, University of Georgia

 Bullying is summarized as unwanted acts of aggression that are likely to be repeated and is difficult to detect through traditional means. This work explored… (more)

Subjects/Keywords: Graph Database; Twitter; Bullying; Sentiment Analysis; Emotional Lexicon; Data Mining.

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

APA (6th Edition):

Patch, J. A. (2015). Detecting bullying on Twitter using emotion lexicons. (Masters Thesis). University of Georgia. Retrieved from http://purl.galileo.usg.edu/uga_etd/patch_jerrad_a_201505_ms

Chicago Manual of Style (16th Edition):

Patch, Jerrad Arthur. “Detecting bullying on Twitter using emotion lexicons.” 2015. Masters Thesis, University of Georgia. Accessed December 15, 2019. http://purl.galileo.usg.edu/uga_etd/patch_jerrad_a_201505_ms.

MLA Handbook (7th Edition):

Patch, Jerrad Arthur. “Detecting bullying on Twitter using emotion lexicons.” 2015. Web. 15 Dec 2019.

Vancouver:

Patch JA. Detecting bullying on Twitter using emotion lexicons. [Internet] [Masters thesis]. University of Georgia; 2015. [cited 2019 Dec 15]. Available from: http://purl.galileo.usg.edu/uga_etd/patch_jerrad_a_201505_ms.

Council of Science Editors:

Patch JA. Detecting bullying on Twitter using emotion lexicons. [Masters Thesis]. University of Georgia; 2015. Available from: http://purl.galileo.usg.edu/uga_etd/patch_jerrad_a_201505_ms


University of Alberta

27. Jocksch, Adam. Data mining flow graphs in a dynamic compiler.

Degree: MS, Department of Computing Science, 2009, University of Alberta

 This thesis introduces FlowGSP, a general-purpose sequence mining algorithm for flow graphs. FlowGSP ranks sequences according to the frequency with which they occur and according… (more)

Subjects/Keywords: Hardware counter; Parallel; Flow graph; Data mining; Compiler

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

APA (6th Edition):

Jocksch, A. (2009). Data mining flow graphs in a dynamic compiler. (Masters Thesis). University of Alberta. Retrieved from https://era.library.ualberta.ca/files/d791sg75w

Chicago Manual of Style (16th Edition):

Jocksch, Adam. “Data mining flow graphs in a dynamic compiler.” 2009. Masters Thesis, University of Alberta. Accessed December 15, 2019. https://era.library.ualberta.ca/files/d791sg75w.

MLA Handbook (7th Edition):

Jocksch, Adam. “Data mining flow graphs in a dynamic compiler.” 2009. Web. 15 Dec 2019.

Vancouver:

Jocksch A. Data mining flow graphs in a dynamic compiler. [Internet] [Masters thesis]. University of Alberta; 2009. [cited 2019 Dec 15]. Available from: https://era.library.ualberta.ca/files/d791sg75w.

Council of Science Editors:

Jocksch A. Data mining flow graphs in a dynamic compiler. [Masters Thesis]. University of Alberta; 2009. Available from: https://era.library.ualberta.ca/files/d791sg75w


The Ohio State University

28. Faisal, S M. Towards Energy Efficient Data Mining & Graph Processing.

Degree: PhD, Computer Science and Engineering, 2015, The Ohio State University

 Ever increasing energy cost is one of the most critical concerns for large scale deployments of data centers. As the demand for large scale data(more)

Subjects/Keywords: Computer Science; Energy Efficiency; Data Mining; Graph Processing; Energy Efficient Computing

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

APA (6th Edition):

Faisal, S. M. (2015). Towards Energy Efficient Data Mining & Graph Processing. (Doctoral Dissertation). The Ohio State University. Retrieved from http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739

Chicago Manual of Style (16th Edition):

Faisal, S M. “Towards Energy Efficient Data Mining & Graph Processing.” 2015. Doctoral Dissertation, The Ohio State University. Accessed December 15, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739.

MLA Handbook (7th Edition):

Faisal, S M. “Towards Energy Efficient Data Mining & Graph Processing.” 2015. Web. 15 Dec 2019.

Vancouver:

Faisal SM. Towards Energy Efficient Data Mining & Graph Processing. [Internet] [Doctoral dissertation]. The Ohio State University; 2015. [cited 2019 Dec 15]. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739.

Council of Science Editors:

Faisal SM. Towards Energy Efficient Data Mining & Graph Processing. [Doctoral Dissertation]. The Ohio State University; 2015. Available from: http://rave.ohiolink.edu/etdc/view?acc_num=osu1440364739


Penn State University

29. Yuan, Dayu. Mining and indexing graph databases.

Degree: PhD, Computer Science and Engineering, 2013, Penn State University

 Graphs are widely used to model structures and relationships of objects in various scientific and commercial fields. Chemical molecules, proteins, malware system-call dependencies and three-dimensional… (more)

Subjects/Keywords: data mining; graph database; query processing; index structure; information retrieval

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

APA (6th Edition):

Yuan, D. (2013). Mining and indexing graph databases. (Doctoral Dissertation). Penn State University. Retrieved from https://etda.libraries.psu.edu/catalog/19880

Chicago Manual of Style (16th Edition):

Yuan, Dayu. “Mining and indexing graph databases.” 2013. Doctoral Dissertation, Penn State University. Accessed December 15, 2019. https://etda.libraries.psu.edu/catalog/19880.

MLA Handbook (7th Edition):

Yuan, Dayu. “Mining and indexing graph databases.” 2013. Web. 15 Dec 2019.

Vancouver:

Yuan D. Mining and indexing graph databases. [Internet] [Doctoral dissertation]. Penn State University; 2013. [cited 2019 Dec 15]. Available from: https://etda.libraries.psu.edu/catalog/19880.

Council of Science Editors:

Yuan D. Mining and indexing graph databases. [Doctoral Dissertation]. Penn State University; 2013. Available from: https://etda.libraries.psu.edu/catalog/19880


Virginia Tech

30. Dos Santos Jr., Raimundo Fonseca. Effective Methods of Semantic Analysis in Spatial Contexts.

Degree: PhD, Computer Science, 2014, Virginia Tech

 With the growing spread of spatial data, exploratory analysis has gained a considerable amount of attention. Particularly in the fields of Information Retrieval and Data(more)

Subjects/Keywords: spatial data; social networks; graph mining; semantic analysis

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

APA (6th Edition):

Dos Santos Jr., R. F. (2014). Effective Methods of Semantic Analysis in Spatial Contexts. (Doctoral Dissertation). Virginia Tech. Retrieved from http://hdl.handle.net/10919/49697

Chicago Manual of Style (16th Edition):

Dos Santos Jr., Raimundo Fonseca. “Effective Methods of Semantic Analysis in Spatial Contexts.” 2014. Doctoral Dissertation, Virginia Tech. Accessed December 15, 2019. http://hdl.handle.net/10919/49697.

MLA Handbook (7th Edition):

Dos Santos Jr., Raimundo Fonseca. “Effective Methods of Semantic Analysis in Spatial Contexts.” 2014. Web. 15 Dec 2019.

Vancouver:

Dos Santos Jr. RF. Effective Methods of Semantic Analysis in Spatial Contexts. [Internet] [Doctoral dissertation]. Virginia Tech; 2014. [cited 2019 Dec 15]. Available from: http://hdl.handle.net/10919/49697.

Council of Science Editors:

Dos Santos Jr. RF. Effective Methods of Semantic Analysis in Spatial Contexts. [Doctoral Dissertation]. Virginia Tech; 2014. Available from: http://hdl.handle.net/10919/49697

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