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Títol: Exploratory data analysis using network based techniques
Autor: Granell Martorell, Clara
Tutor/director/avaluador: Arenas Moreno, Alex; Gómez Jiménez, Sergio
Universitat: Universitat Politècnica de Catalunya
Universitat Rovira i Virgili
Matèries: Àrees temàtiques de la UPC::Matemàtiques i estadística::Estadística matemàtica
Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Representació del coneixement
Mathematical statistics
Data--Classification
Estadística matemàtica
Dades--Classificació
Data: 1-set-2012
Tipus de document: Master thesis
Descripció: 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.
URI: http://hdl.handle.net/2099.1/16438
Condicions d'accés: Open Access
Apareix a les col·leccions:Master in Artificial Intelligence - MAI (Pla 2006)
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