Uso de redes complexas na classificação relacional

Abstract

The vast amount of information available on any area of knowledge makes selecting and analyzing information on a specific topic increasingly difficult. Therefore, it is necessary the improvement of techniques for automatic information retrieval, analysis, and knowledge extraction from data sets. In this scenario, especial attention must be addressed for Machine Learning and Data Mining researches. In machine learning and data mining, most of the techniques uses a propositional representation, which considers only the characteristics of the objects described into an attribute-value table. However, there are domains where, in addition to the description of the objects, it is also available information about relationship between them. Such domains can be represented by graphs where vertices represent objects and edges relationship between objects, enabling the application of techniques for relational data. Concepts of complex networks (CN) can be useful in this context. CN is a recent and active research field, which studies the behavior of many real systems modeled by graphs. However, there is little work in machine learning or data mining applying CN concepts. This project presents a proposal to use the formalism of complex networks and graphs to discover patterns in the context of supervised learning. The formalism of graphs can represent relationships between objects and characteristics of the domain, allowing adding structural knowledge embedded in a graph into the data mining process. Specifically, this work develops a relational representation based on graphs constructed taking into consideration the similarity between objects. Based on this representation, relational classification approaches are proposed. It is also proposed a network referred to K-Associate Network. Properties of the K-Associate Network were investigated. The experimental results show great potential for the proposed classification and network construction algorithms. Esta dissertacao foi preparada com o formatador de textos LTEX. Foi utilizado um estilo (style) desenvolvido por Ronaldo Cristiano Prati. O sistema de citacoes de referencias bibliograficas utiliza o padrao Chicago do sistema BibTEX. Algumas palavras utilizadas neste trabalho nao foram traduzidas da ĺingua inglesa para a portuguesa por serem amplamente conhecidas e difundidas na comunidade academica.