Abstract
In Multi-RelationalDataMining (MRDM), data are represented in a relational form where the individuals of the target table are potentially related to several records in secondary tables in one-to-many relationship. Variable pre-processing (including discretization and feature selection) within this multiple table setting differs from the attribute-value case. Besides the target variable information, one should take into account the relational structure of the database. In this paper, we focus on numerical variables located in a non target table. We propose a criterion that evaluates a given discretization of such variables. The idea is to summarize for each individual the information contained in the secondary variable by a feature tuple (one feature per interval of the considered discretization). Each feature represents the number of values of the secondary variable ranging in the corresponding interval. These count features are jointly partitioned by means of data grid models in order to obtain the best separation of the class values. We describe a simple optimization algorithm to find the best equal frequency discretization with respect to the proposed criterion. Experiments on a real and artificial data sets reveal that the discretization approach helps one to discover relevant secondary variables.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Alfred, R.: Discretization Numerical Data for Relational Data with One-to-Many Relations. Journal of Computer Science 5(7), 519–528 (2009)
Boullé, M.: MODL: A Bayes optimal discretization method for continuous attributes. Machine Learning 65(1), 131–165 (2006)
Boullé, M.: Data Grid Models for Preparation and Modeling in Supervised Learning. In: Guyon, I., Cawley, G., Dror, G., Saffari, A. (eds.) Hand on Pattern Recognition: Challenges in Machine Learning, pp. 99–130. Microtome Publishing (2011)
Džeroski, S.: Inductive logic programming and knowledge discovery in databases. In: Advances in Knowledge Discovery and Data Mining, pp. 117–152. American Association for Artificial Intelligence, Menlo Park (1996)
Fawcett, T.: ROC graphs: Notes and practical considerations for researchers. Technical report, Technical Report HPL-2003-4. Hewlett Packard Laboratories (2003)
Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: Thirteenth International Joint Conference on Artificial Intelligence, pp. 1022–1027 (1993)
Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)
Knobbe, A.J., Blockeel, H., Siebes, A., Van Der Wallen, D.: Multi-Relational Data Mining. In: Proceedings of Benelearn 1999 (1999)
Knobbe, A.J., Ho, E.K.Y.: Numbers in multi-relational data mining. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 544–551. Springer, Heidelberg (2005)
Kramer, S., Flach, P.A., Lavrač, N.: Propositionalization approaches to relational data mining. In: Džeroski, S., Lavrač, N. (eds.) Relational Data Mining, ch. 11, pp. 262–286. Springer, New York (2001)
Provost, F., Domingos, P.: Well-trained pets: Improving probability estimation trees. Technical Report CeDER #IS-00-04, New York University (2001)
Shannon, C.: A mathematical theory of communication. Technical report. Bell Systems Technical Journal (1948)
Van Laer, W., De Raedt, L., Džeroski, S.: On multi-class problems and discretization in inductive logic programming. In: Raś, Z.W., Skowron, A. (eds.) ISMIS 1997. LNCS, vol. 1325, pp. 277–286. Springer, Heidelberg (1997)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Lahbib, D., Boullé, M., Laurent, D. (2014). Supervised Pre-processing of Numerical Variables for Multi-Relational Data Mining. In: Guillet, F., Pinaud, B., Venturini, G., Zighed, D. (eds) Advances in Knowledge Discovery and Management. Studies in Computational Intelligence, vol 527. Springer, Cham. https://doi.org/10.1007/978-3-319-02999-3_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-02999-3_6
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-02998-6
Online ISBN: 978-3-319-02999-3
eBook Packages: EngineeringEngineering (R0)