Exploring Multiple Clusterings In Attributed Graphs

O professor Eduardo Ogasawara apresentou o trabalho intitulado “Exploring Multiple Clusterings In Attributed Graphs” no ACM Symposium of Applied Computing (SAC 2015). O trabalho está no escopo do doutorado do prof. Gustavo Guedes, orientado pelo prof. Geraldo Xexéo, com colaboração dos professores Eduardo Bezerra e Eduardo Ogasawara.



Many graph clustering algorithms aim at generating a single partitioning (clustering) of the data. However, there can be many ways a dataset can be clustered. From a exploratory analisys perspective, given a dataset, the availability of many di erent and non-redundant clusterings is important for data understanding. Each one of these clusterings could provide a di erent insight about the data. In this paper, we propose M-CRAG, a novel algorithm that
generates multiple non-redundant clusterings from an attributed graph. We focus on attributed graphs, in which each vertex is associated to a n-tuple of attributes (e.g., in a social network, users have interests, gender, age, etc.). M-CRAG adds arti cial edges between similar vertices of the attributed graph, which results in an augmented attributed graph. This new graph is then given as input to our clustering algorithm (CRAG). Experimental results indicate that M-CRAG is e ective in providing multiple clusterings from
an attributed graph.

Artigo completo

Comments are closed.