Classification for Multi-Relational Data Mining Using Bayesian Belief Network

Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 27)


Multi-Relational Data Mining is an active area of research from last decade. Relational database is an important source of structured data, hence richest source of knowledge. Most of the commercial and application oriented data uses a relational database scheme in which multiple relations are linked through primary key, foreign key relationship. Multi-Relational Data Mining (MRDM) deals with extraction of information from a relational database containing multiple tables related to each other. In order to extract important information or knowledge, it is required to apply Data Mining algorithms on this relational database but most of these algorithms work only on a single table. Generating a single table from multiple tables may result in loss of important information, like the relation between tuples, also it is a not efficient in terms of time and space. In this paper, we proposed a Probabilistic Graphical Model, Bayesian Belief Network (BBN), based approach that considers not only attributes of the table but also the relation between tables. The conditional dependencies between tables are derived from Semantic Relationship Graph (SRG) of the relational database, whereas Tuple Id propagation helps to derive the conditional probability of tables. Our model not only predicts class label of unknown samples, but also gives the value of sample if class label is known.


Multi-Relational Data Mining Relational database Bayesian Belief Network Data Mining Probabilistic graphical model Semantic Relationship Graph Tuple Id propagation 


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  1. 1.
    Sašo, D., Lavrač, N.: An introduction to inductive logic programming. In: Relational Data Mining, pp. 48–73 (2001)Google Scholar
  2. 2.
    Lavrac, N., Dzeroski, S.: Inductive Logic Programming: Techniques and Applications. Ellis Horwood (1994)Google Scholar
  3. 3.
    Blockeel, H., Dehaspe, L., Demoen, B., Janssens, G., Ramon, J., Vandecasteele, H.: Improving the Efficiency of Inductive Logic Programming through the Use of Query Packs. J. Artificial Intelligence Research 16, 135–166 (2002)MATHGoogle Scholar
  4. 4.
    Yin, X., Han, J., Yang, J., Yu, P.S.: CrossMine: Efficient classification across multiple database relations. In: Boulicaut, J.-F., De Raedt, L., Mannila, H. (eds.) Constraint-Based Mining. LNCS (LNAI), vol. 3848, pp. 172–195. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Muggleton, S.H.: Inverse Entailment and Progol. New Generation Computing 13(3-4), 245–286 (1995)CrossRefGoogle Scholar
  6. 6.
    Muggleton, S., Feng, C.: Efficient Induction of Logic Programs. In: Proceedings of Conference on Algorithmic Learning Theory (1990)Google Scholar
  7. 7.
    Pompe, U., Kononenko, I.: Naive Bayesian classifier within ILP-R. In: Proceedings of the 5th International Workshop on Inductive Logic Programming, pp. 417–436 (1995)Google Scholar
  8. 8.
    Heckerman, D.: Bayesian networks for data mining. Data Mining and Knowledge Discovery 1(1), 79–119 (1997)CrossRefGoogle Scholar
  9. 9.
    Ceci, M., Appice, A., Malerba, D.: Mr-SBC: A Multi-relational Naïve Bayes Classifier. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds.) PKDD 2003. LNCS (LNAI), vol. 2838, pp. 95–106. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  10. 10.
    Flach, P., Lachiche, N.: 1BC: A first-order Bayesian classifier. In: Džeroski, S., Flach, P.A. (eds.) ILP 1999. LNCS (LNAI), vol. 1634, pp. 92–103. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  11. 11.
    Neville, J., Jensen, D., Gallagher, B., Fairgrieve, R.: Simple Estimators for Relational Bayesian Classifiers. In: International Conference on Data Mining (2003)Google Scholar
  12. 12.
    Manjunath, G., Murty, M.N., Sitaram, D.: Combining heterogeneous classifiers for relational databases. Pattern Recognition 46(1), 317–324 (2013)CrossRefGoogle Scholar
  13. 13.
    Quinlan, J.R., Cameron-Jones, R.M.: FOIL: A Midterm Report. In: Proceedings of 1993 European Conference on Machine Learning (1993)Google Scholar
  14. 14.
    Yin, X., Han, J., Yang, J.: Efficient Multi-relational Classification by Tuple ID Propagation. In: Proceedings of the KDD-2003 Workshop on Multi-Relational Data Mining (2003)Google Scholar

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© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Department of Information TechnologyDharmsinh Desai UniversityNadiadIndia

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