Abstract
Most aggregation functions are limited to either categorical or numerical values but not both values. In this paper, we define three concepts of aggregation function and introduce a novel method to aggregate multiple instances that consists of both the categorical and numerical values. We show how these concepts can be implemented using clustering techniques. In our experiment, we discretize continuous values before applying the aggregation function on relational datasets. With the empirical results obtained, we demonstrate that our transformation approach using clustering techniques, as a means of aggregating multiple instances of attribute’s values, can compete with existing multi-relational techniques, such as Progol and Tilde. In addition, the effect of the number of interval for discretization on the classification performance is also evaluated.
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References
Alfred, R., Kazakov, D.: Pattern-Based Transformation Approach to Relational Domain Learning Using DARA. In: Crone, S.F., Lessmann, S., Stahlbock, R. (eds.) Proceedings of the International Conference on Data Mining, LAS VEGAS, Nevada, June 25-29, 2006, pp. 296–302. CSREA Press (2006)
Srinivasan, A., Muggleton, S.H., Sternberg, M.J.E., King, R.D.: Theories for mutagenicity: A Study in first-order and feature-based induction. Artificial Intelligence, 85 (1996)
Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Artificial Intelligence 89(1-2), 31–71 (1997)
Żytkow, J.M., Rauch, J. (eds.): Principles of Data Mining and Knowledge Discovery. LNCS (LNAI), vol. 1704. Springer, Heidelberg (1999)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, Los Altos, Cal
Kramer, S., Lavrač, N., Flach, P.: Propositionalization approaches to relational data mining. In: Dzeroski, S., Lavrač, N. (eds.) Relational Data mining, Springer, Heidelberg (2001)
Salton, G., Michael, J.: Introduction to Modern Information Retrieval. McGraw-Hill, Inc., New York (1986)
Bezdek, J.C.: Some new indexes of cluster validity. IEEE Transaction System, Man, Cybern. B 28, 301–315 (1998)
Boley, D.: Principal direction divisive partitioning. Data Mining and Knowledge Discovery 2(4), 325–344 (1998)
Witten, I., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufman, Seattle (1999)
Knobbe, A.J., de Haas, M., Siebes, A.: Propositionalisation and Aggregates. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 277–288. Springer, Heidelberg (2001)
Perlich, C., Provost, F.: Aggregation-Based Feature Invention and Relational Concept Classes. In: KDD 2003. Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ACM Press, New York (2003)
De Raedt, L.: Attribute-value learning versus inductive logic programming: The missing links (extended abstract). In: Page, D.L. (ed.) Inductive Logic Programming. LNCS, vol. 1446, pp. 1–8. Springer, Heidelberg (1998)
Lavrač, N., Džeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood (1994)
Lavrač, N., Džeroski, S., Grobelnik, M.: Learning nonrecursive definitions of relations with LINUS. In: Kodratoff, Y. (ed.) Machine Learning - EWSL-91. LNCS, vol. 482, pp. 265–281. Springer, Heidelberg (1991)
Krogel, M.A., Wrobel, S.: Transformation-Based Learning Using Multirelational Aggregation. In: Rouveirol, C., Sebag, M. (eds.) ILP 2001. LNCS (LNAI), vol. 2157, Springer, Heidelberg (2001)
Lavrač, N., Železny, F., Flach, P.A.: RSD: Relational subgroup discovery through first-order feature construction. In: Matwin, S., Sammut, C. (eds.) ILP 2002. LNCS (LNAI), vol. 2583, Springer, Heidelberg (2003)
Krogel, M.A., Rawles, S., Železny, F., Flach, P.A., Lavrač, N., Wrobel, S.: Comparative evaluation of approaches to propositionalization. In: Horváth, T., Yamamoto, A. (eds.) ILP 2003. LNCS (LNAI), vol. 2835, pp. 197–214. Springer, Heidelberg (2003)
Blokceel, H., Bruynooghe, M.: Aggregation versus Selection Bias, and relational neural networks. In: Kurumatani, K., Chen, S.-H., Ohuchi, A. (eds.) IJCAI-WS 2003 and MAMUS 2003. LNCS (LNAI), vol. 3012, Springer, Heidelberg (2004)
Agrawal, R., Gehrke, J., Gunopulos, D., Raghavan, P.: Automatic subspace clustering of high dimensional data for data mining applications. In: Proceedings of the 1998 ACM SIGMOD International Conference on Management of Data, pp. 94–105. ACM Press, New York (1998)
Hofmann, T., Buhnmann, J.M.: Active data clustering. In: Advance in Neural Information Processing System (1998)
Hartigan, J.A.: Clustering Algorithms. Wiley, New York (1975)
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Alfred, R. (2007). The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining. In: Alhajj, R., Gao, H., Li, J., Li, X., Zaïane, O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science(), vol 4632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73871-8_21
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DOI: https://doi.org/10.1007/978-3-540-73871-8_21
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