Cited By
View all- Guedes GOgasawara EBezerra EXexeo G(2016)Discovering top-k non-redundant clusterings in attributed graphsNeurocomputing10.1016/j.neucom.2015.10.145210:C(45-54)Online publication date: 19-Oct-2016
Exploring multiple clusterings in attributed graphs
Authors: 
Gustavo Paiva Guedes, Eduardo Bezerra, Eduardo Ogasawara, Geraldo XexéoAuthors Info & Claims
Gustavo Paiva Guedes, Eduardo Bezerra, Eduardo Ogasawara, Geraldo XexéoAuthors Info & ClaimsPages 915 - 918
Abstract
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 different and non-redundant clusterings is important for data understanding. Each one of these clusterings could provide a different 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 Artificial 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 effective in providing multiple clusterings from an attributed graph.
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- Exploring multiple clusterings in attributed graphs
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April 2015
2418 pages
ISBN:9781450331968
DOI:10.1145/2695664
- Conference Chairs:
- Roger L. Wainwright,
- Juan Manuel Corchado,
- Program Chairs:
- Alessio Bechini,
- Jiman Hong
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Published: 13 April 2015
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View all- Guedes GOgasawara EBezerra EXexeo G(2016)Discovering top-k non-redundant clusterings in attributed graphsNeurocomputing10.1016/j.neucom.2015.10.145210:C(45-54)Online publication date: 19-Oct-2016
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