- João Carlos Xavier-Júnior21,
- Anne M. P. Canuto21,
- Luiz M. G. Gonçalves22 &
- …
- Luiz A. H. G. de Oliveira22
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Abstract
A wide range of the database systems in use today are based on the relational model. As a consequence, more information used by those systems has been stored in multi relational object types. However, most of the traditional machine learning algorithms have not been originally proposed to handle this type of data. Aiming to propose better ways of handling the relational particularities of the data, this paper proposes a new relational clustering method based on relationship and attribute information. In our method, attributes have weights associated with their importance between the object types. An empirical analysis is performed in order to evaluate the effectiveness of the proposed method, comparing with two traditional methods for relational clustering. Three relational databases were used in the experiments.
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Authors and Affiliations
Informatics and Applied Mathematics Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil, 59072-970
João Carlos Xavier-Júnior & Anne M. P. Canuto
Computing and Automation Engineering Department, Federal University of Rio Grande do Norte, Natal, RN, Brazil, 59078-900
Luiz M. G. Gonçalves & Luiz A. H. G. de Oliveira
- João Carlos Xavier-Júnior
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- Anne M. P. Canuto
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- Luiz M. G. Gonçalves
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- Luiz A. H. G. de Oliveira
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Editors and Affiliations
Neuro Heuristic Research Group, University of Lausanne, 1015, Lausanne, Switzerland
Alessandro E. P. Villa
Department of Informatics, Nicolaus Copernicus University, 87-100, Toruń, Poland
Włodzisław Duch
Center for Complex Systems Studies, Kalamazoo College, 49006, Kalamazoo, MI, USA
Péter Érdi
Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, 16146, Genoa, Italy
Francesco Masulli
Institut für Neuroinformatik, Universität Ulm, 89069, Ulm, Germany
Günther Palm
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Xavier-Júnior, J.C., Canuto, A.M.P., Gonçalves, L.M.G., de Oliveira, L.A.H.G. (2012). New Approach for Clustering Relational Data Based on Relationship and Attribute Information. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds) Artificial Neural Networks and Machine Learning – ICANN 2012. ICANN 2012. Lecture Notes in Computer Science, vol 7553. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33266-1_56
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