Exploratory data analysis using network based techniques

The aim of this document is to present the work done during the development of my master thesis. The work belongs to the field of complex networks, more concretely to the detection of communities in complex networks. Chapter 1 will be an introduction of the basic concepts and motivations of this wor...

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Detalles Bibliográficos
Autor: Granell Martorell, Clara
Tipo de recurso: tesis de maestría
Fecha de publicación:2012
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2099.1/16438
Acceso en línea:https://hdl.handle.net/2099.1/16438
Access Level:acceso abierto
Palabra clave:Mathematical statistics
Data--Classification
Estadística matemàtica
Dades--Classificació
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
id ES_0c02bd78305071f88d7d6912372437e2
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network_acronym_str ES
network_name_str España
repository_id_str
spelling Exploratory data analysis using network based techniquesGranell Martorell, ClaraMathematical statisticsData--ClassificationEstadística matemàticaDades--ClassificacióÀrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàticaÀrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixementThe aim of this document is to present the work done during the development of my master thesis. The work belongs to the field of complex networks, more concretely to the detection of communities in complex networks. Chapter 1 will be an introduction of the basic concepts and motivations of this work, mainly clarifying the fields of exploratory data analysis, data clustering and complex networks. As all the work is about the finding of communities in complex networks, Chapter 2 is devoted to explain the concepts of mesoscopic structure of networks and its importance in the analysis of real networks, along with the explanations of some of the most well-known techniques to perform this analysis. All the progress done during the master thesis relies on a method for detecting communities developed in the past years by the research group I belong to. This method is known as the AFG algorithm, named after the three authors Arenas, Fernández and Gómez, and it is explained in section 2.5.2 with special emphasis. The work that I have developed is composed of two separate problems: the first one consists in designing an application to make possible the use of the AFG community detection method to perform data clustering over real world multidimensional datasets, which is explained in Chapter 3. The second work consists in improving the AFG method to make possible the detection of communities even when the difference of sizes of the communities make their detection impossible for other community detection algorithms, which can be found in Chapter 4. Chapter 5 contains the conclusions and the future lines of research derived from the present work, and in the Appendix there is a list of publications that sustain the contents presented in this document.Universitat Politècnica de CatalunyaArenas Moreno, AlexGómez Jiménez, Sergio20122012-09-0120122012-10-30master thesishttp://purl.org/coar/resource_type/c_bdccNAhttp://purl.org/coar/version/c_be7fb7dd8ff6fe43info:eu-repo/semantics/masterThesisapplication/pdfhttps://hdl.handle.net/2099.1/16438reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)Inglésengopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivs 3.0 Spainhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2099.1/164382026-05-27T15:37:01Z
dc.title.none.fl_str_mv Exploratory data analysis using network based techniques
title Exploratory data analysis using network based techniques
spellingShingle Exploratory data analysis using network based techniques
Granell Martorell, Clara
Mathematical statistics
Data--Classification
Estadística matemàtica
Dades--Classificació
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
title_short Exploratory data analysis using network based techniques
title_full Exploratory data analysis using network based techniques
title_fullStr Exploratory data analysis using network based techniques
title_full_unstemmed Exploratory data analysis using network based techniques
title_sort Exploratory data analysis using network based techniques
dc.creator.none.fl_str_mv Granell Martorell, Clara
author Granell Martorell, Clara
author_facet Granell Martorell, Clara
author_role author
dc.contributor.none.fl_str_mv Arenas Moreno, Alex
Gómez Jiménez, Sergio
dc.subject.none.fl_str_mv Mathematical statistics
Data--Classification
Estadística matemàtica
Dades--Classificació
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
topic Mathematical statistics
Data--Classification
Estadística matemàtica
Dades--Classificació
Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
description The aim of this document is to present the work done during the development of my master thesis. The work belongs to the field of complex networks, more concretely to the detection of communities in complex networks. Chapter 1 will be an introduction of the basic concepts and motivations of this work, mainly clarifying the fields of exploratory data analysis, data clustering and complex networks. As all the work is about the finding of communities in complex networks, Chapter 2 is devoted to explain the concepts of mesoscopic structure of networks and its importance in the analysis of real networks, along with the explanations of some of the most well-known techniques to perform this analysis. All the progress done during the master thesis relies on a method for detecting communities developed in the past years by the research group I belong to. This method is known as the AFG algorithm, named after the three authors Arenas, Fernández and Gómez, and it is explained in section 2.5.2 with special emphasis. The work that I have developed is composed of two separate problems: the first one consists in designing an application to make possible the use of the AFG community detection method to perform data clustering over real world multidimensional datasets, which is explained in Chapter 3. The second work consists in improving the AFG method to make possible the detection of communities even when the difference of sizes of the communities make their detection impossible for other community detection algorithms, which can be found in Chapter 4. Chapter 5 contains the conclusions and the future lines of research derived from the present work, and in the Appendix there is a list of publications that sustain the contents presented in this document.
publishDate 2012
dc.date.none.fl_str_mv 2012
2012-09-01
2012
2012-10-30
dc.type.none.fl_str_mv master thesis
http://purl.org/coar/resource_type/c_bdcc
NA
http://purl.org/coar/version/c_be7fb7dd8ff6fe43
dc.type.openaire.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
dc.identifier.none.fl_str_mv https://hdl.handle.net/2099.1/16438
url https://hdl.handle.net/2099.1/16438
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivs 3.0 Spain
http://creativecommons.org/licenses/by-nc-nd/3.0/es/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universitat Politècnica de Catalunya
publisher.none.fl_str_mv Universitat Politècnica de Catalunya
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,300719

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