Dimensionality Reduction in Data Summarization Approach to Learning Relational Data

  • Chung Seng Kheau
  • Rayner Alfred
  • Lau Hui Keng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7802)


Due to the growing amount of digital data stored in relational databases, more new approaches are required to learn relational data. The DARA algorithm is designed to summarize data and it is one of the approaches introduced in relational data mining in order to handle data with one-to-many relations. The DARA algorithm transforms data stored in relational databases into a vector space representation by applying the information retrieval theory. Based on the experimental results, the DARA algorithm is proven to be very effective in learning relational data. However, DARA suffers a major drawback when the cardinalities of attributes are very high because the size of the vector space representation depends on the number of unique values that exist for all attributes in the dataset. This paper investigates the effects of discretizing the magnitude of terms computed and applying a feature selection process that reduces the cardinalities of attributes of the relational datasets on the predictive accuracy of the overall classification task. This involves the task of finding the best set of relevant features used to summarize the data, in which the feature selection processed is performed based on the magnitude of terms computed earlier. Based on the results obtained, it shows that the predictive accuracy of the classification task can be improved by improving the quality of the summarized data. The quality of the summarized data can be enhanced by appropriately discretizing the magnitude of terms computed earlier and also appropriately selecting only a certain percentage of the attributes.


Relational Data Mining Data Summarization Clustering Dimensionality Reduction Discretization Numbers Feature Selection 


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  1. 1.
    Quinlan, J.R.: Learning Logical Definitions from Relations. Machine Learn. 5(3), 239–266 (1990)Google Scholar
  2. 2.
    Mitra, P., Murthy, C.A., Pal, S.K.: Unsupervised feature selection using feature similarity. Pattern Analysis and Machine Intelligence 24(3), 301–312 (2002)CrossRefGoogle Scholar
  3. 3.
    Miller: Subset Selection in Regression, 2nd edn. Chapman & Hall (2002)Google Scholar
  4. 4.
    Quinlan, R.J.: C4.5: Programs for Machine Learning. Morgan Kaufmainn Series in Machine Learning (1993)Google Scholar
  5. 5.
    Srinivasan, A., Muggleton, S., Sternberg, M.J.E., King, R.D.: Theories for Mutagenicity: A Study in First-Order and Feature-Based Induction. Artificial Intelligence 85(1-2), 277–299 (1996)CrossRefGoogle Scholar
  6. 6.
    Harigan, J.A.: Clustering Algorithms. John Wiley, New York (1775)Google Scholar
  7. 7.
    Witten, I.H., Frank, E.: Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann (1999)Google Scholar
  8. 8.
    Alfred, R.: The Study of Dynamic Aggregation of Relational Attributes on Relational Data Mining. In: Alhajj, R., Gao, H., Li, X., Li, J., Zaïane, O.R. (eds.) ADMA 2007. LNCS (LNAI), vol. 4632, pp. 214–226. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  9. 9.
    Alfred, R.: Optimizing feature construction process for dynamic aggregation of relational attributes. J. Comput. Sci. 5, 864–877 (2009), doi:10.3844/jcssp.2009.864.877CrossRefGoogle Scholar
  10. 10.
    Alfred, R.: Summarizing relational data using semi-supervised genetic algorithm-based clustering techniques. Journal of Computer Science 6(7), 775–784 (2010)CrossRefGoogle Scholar
  11. 11.
    Alfred, R.: Feature transformation: A genetic-based feature construction method for data summarization. Computational Intelligence 26(3), 337–357 (2010)MathSciNetMATHCrossRefGoogle Scholar
  12. 12.
    Salton, G., Michael, J.: McGill, Introduction to Modern Information Retrieval. McGraw-Hill Inc., New York (1986)Google Scholar
  13. 13.
    Karunaratne, T., Bostrom, H., Norinder, U.: Pre-Processing Structured Data for Standard Machine Learning Algorithms by Supervised Graph Propositionalization – a Case Study with Medicinal Chemistry Datasets. In: Ninth International Conference on Machine Learning and Applications, pp. 828–833 (2010)Google Scholar
  14. 14.
    Muggleton, SH.: Learning Stochastic Logic Programs, In Proceedings of the AAAI 2000 Workshop on Learning Statistical Models from Relational Data, Technical Report WS-00-06, pp.36-41 (2000)Google Scholar
  15. 15.
    Zhang, C., Wang, J.: Multi-relational Bayesian Classification Algorithm with Rough Set. In: 7th Intl. Conf. Of FSKD 2010, pp. 1565–1568 (2010)Google Scholar
  16. 16.
    Li, Y., Luan, L., Sheng, Y., Yuan, Y.: Multi-relational Classification Based on the Contribution of Tables. In: International Conference on Artificial Intelligence and Computational Intelligence, pp. 370–374 (2009)Google Scholar
  17. 17.
    Cao, P., Hong-yuan, W.: Multi-relational Classification on the Basic of the Attribute Reduction Twice. Communication and Computer 6(11), 49–52 (2009)Google Scholar
  18. 18.
    He, J., Liu, H., Hu, B., Du, X., Wang, P.: Selecting Effective Features and Relations For Efficient Multi-Relational Classification. Computational Intelligence 26(3), 1467–8640 (2010)MathSciNetCrossRefGoogle Scholar
  19. 19.
    Guo, H., Herna, L.: Viktor: Multi-relational classification: a multiple view approach. Knowl. Inf. Systems 17, 287–312 (2008)CrossRefGoogle Scholar
  20. 20.
    Wrobel, S.: Inductive Logic Programming for Knowledge Discovery in Databases: Relational Data Mining, pp. 74–101. Springer, Berlin (2001)Google Scholar
  21. 21.
    Emce, W., Wettschereck, D.: Relational instance-based learning. In: Proceedings of the Thirteenth International Conference on Machine Learning, pp. 122–130. Morgan Kaufmann, San Matco (1996)Google Scholar
  22. 22.
    Kirsten, M., Wrobel, S., Horvath, T.: Relational Distance Based Clustering. In: 8th International Conference on Inductive Logic Programming, pp.261–270 (1998, 2001)Google Scholar
  23. 23.
    Woznica, A., Kalousis, A., Hilorio, M.: Kernel-based distances for relational learning. In: Proceedings of the Workshop on Multi-Relational Data Mining at KDD (2004)Google Scholar
  24. 24.
    Getoor, L.: Multi-relational data mining using probalilistic relational models: research summary. In: Proceedings of the First Workshop in Multi-relational Data Mining (2001)Google Scholar

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© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Chung Seng Kheau
    • 1
  • Rayner Alfred
    • 1
  • Lau Hui Keng
    • 1
  1. 1.School of Engineering and Information TechnologyUniversiti Malaysia SabahKota KinabaluMalaysia

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