Cluster in graphs is densely connected group of vertices sparsely connected to other
groups. Hence, for prediction of a future link between a pair of vertices, these vertices …

Graph is a powerful representation formalism that has been widely employed in machine
learning and data mining. In this paper, we present a graph-based classification method …

Nos dias atuais há uma quantidade massiva de dados textuais sendo produzida e
armazenada diariamente na forma de e-mails, relatórios, artigos e postagens em redes …

An efficient recognition framework requires both good feature representation and effective
classification methods. This paper proposes such a framework based on a spatial Scale …

Regular data classification techniques are based mainly on two strong assumptions:(1) the
existence of a reasonably large labeled set of data to be used in training; and (2) future input …

Many real-world networks, such as the Internet, social networks, biological networks are
massive in size, which difficult different processing and analysis tasks. For this reason, it is …

Differently from traditional machine learning techniques applied to data classification, high
level classification considers not only the physical features of the data (distance, similarity or …

Data repositories are getting larger and in most of the cases, only a small subset of their data
items is labeled. In such scenario semi-supervised learning (SSL) techniques have become …

Clustering provides structural information from unlabeled data. The studies in which the
structural information of the dataset is obtained through unsupervised learning approaches …

A vasta quantidade de informações disponível sobre qualquer área de conhecimento torna
cada vez mais difícil selecionar e analisar informações específicas e relevantes sobre …