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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References
Alfred, R., Kazakov, D.: Clustering approach to generalized pattern identification based on multi-instanced objects with dara. In: ADBIS Research Communications, pp. 38–49 (2007)
Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence 1(2), 224–227 (1979)
Fisher, R.: Statistical methods for research workers. The Eugenics Review 18(2), 148–150 (1926)
Gao, Y., Liu, D.Y., Sun, C.M., Liu, H.: A two-stage clustering algorithm for multi-type relational data. In: Proceedings of the 2008 Ninth ACIS International Conference on Software Engineering, Artificial Intelligence, Networking, and Parallel/Distributed Computing, pp. 376–380. IEEE Computer Society, Washington, DC (2008)
Harrington, J.L.: Relational Database Design and Implementation: Clearly Explained. Morgan Kaufmann (2009)
Hubert, L., Arabie, P.: Comparing partitions. Journal of Classification 2(1), 193–218 (1985)
Jain, A.K., Murty, M.N., Flynn, P.J.: Data clustering: A review. ACM Comput. Surv. 31(3), 264–323 (1999)
Long, B., Zhang, Z., Yu, P.S.: A probabilistic framework for relational clustering. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 470–479 (2007)
Neville, J., Adler, M., Jensen, D.: Clustering relational data using attribute and link information. In: Proceedings of the Text Mining and Link Analysis Workshop (2003)
Rousseeuw, P.: Silhouettes: a graphical aid to the interpretation and validation of cluster analysis. J. Comput. Appl. Math. 20(1), 53–65 (1987)
Seltzer, M.I.: Beyond relational databases. ACM Queue 3(3), 50–58 (2005)
Xavier-Júnior, J.C., Canuto, A., Freitas, A., Gonçalves, L., Silla-Jr., C.: A hierarchical approach to represent relational data applied to clustering tasks. In: International Joint Conference on Neural Networks, pp. 3055–3062. IEEE Press (2011)
Xavier-Junior, J.C., Signoretti, A., Canuto, A.M.P., Campos, A.M., Gonçalves, L.M.G., Fialho, S.V.: Introducing Affective Agents in Recommendation Systems Based on Relational Data Clustering. In: Hameurlain, A., Liddle, S.W., Schewe, K.-D., Zhou, X. (eds.) DEXA 2011, Part II. LNCS, vol. 6861, pp. 303–310. Springer, Heidelberg (2011)
Yin, X., Han, J., Yu, P.S.: Cross-relational clustering with user’s guidance. In: Proceedings of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining, KDD 2005, pp. 344–353. ACM, New York (2005)
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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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DOI: https://doi.org/10.1007/978-3-642-33266-1_56
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