OBTAINING AND EVALUATING GENERALIZED ASSOCIATION RULES

Veronica Oliveira de Carvalho, Solange Oliveira Rezende, Mário de Castro

2007

Abstract

Generalized association rules are rules that contain some background knowledge giving a more general view of the domain. This knowledge is codified by a taxonomy set over the data set items. Many researches use taxonomies in different data mining steps to obtain generalized rules. So, this work initially presents an approach to obtain generalized association rules in the post-processing data mining step using taxonomies. However, an important issue that has to be explored is the quality of the knowledge expressed by generalized rules, since the objective of the data mining process is to obtain useful and interesting knowledge to support the user’s decisions. In general, what researches do to help the users to select these pieces of knowledge is to reduce the obtained set by pruning some specialized rules using a subjective measure. In this context, this paper also presents a quality analysis of the generalized association rules. The quality of the rules obtained by the proposed approach was evaluated. The experiments show that some knowledge evaluation objective measures are appropriate only when the generalization occurs on one specific side of the rules.

References

  1. Adamo, J.-M. (2001). Data Mining for Association Rules and Sequential Patterns. Springer-Verlag.
  2. Agrawal, R. and Srikant, R. (1994). Fast algorithms for mining association rules. In Bocca, J. B., Jarke, M., and Zaniolo, C., editors, Proceedings of the 20th International Conference on Very Large Data Bases, VLDB'94, pages 487-499.
  3. Baixeries, J., Casas, G., and Balcázar, J. L. (2000). Frequent sets, sequences, and taxonomies: New, efficient algorithmic proposals. Technical Report LSI-00-78- R, Departament de LSI - Universitat Politècnica de Catalunya.
  4. Carvalho, V. O., Rezende, S. O., and Castro, M. (2007a). An analytical evaluation of objective measures behavior for generalized association rules. In IEEE Symposium on Computational Intelligence and Data Mining - CIDM/2007. In Press.
  5. Carvalho, V. O., Rezende, S. O., and Castro, M. (2007b). Evaluating generalized association rules through objective measures. In Devedz?ic, V., editor, IASTED International Conference on Artificial Intelligence and Applications - AIA 2007. ACTA Press.
  6. Chung, F. and Lui, C. (2000). A post-analysis framework for mining generalized association rules with multiple minimum supports. In PostProcessing in Machine Learning and Data Mining: Interpretation, Visualization, Integration, and Related Topics (Workshop within KDD'2000). Retrivied November 17, 2006, from http://www.cs.fit.edu/ pkc/kdd2000ws/post.html.
  7. Domingues, M. A. and Rezende, S. O. (2005). Using taxonomies to facilitate the analysis of the association rules. In Proceedings of ECML/PKDD'05 - The Second International Workshop on Knowledge Discovery and Ontologies (KDO-2005) , pages 59-66.
  8. Han, J. and Fu, Y. (1995). Discovery of multiple-level association rules from large databases. In Dayal, U., Gray, P. M. D., and Nishio, S., editors, Proceedings of 21th International Conference on Very Large Data Bases VLDB'95, pages 420-431.
  9. Han, J. and Fu, Y. (1999). Mining multiple-level association rules in large databases. IEEE Transactions on Knowledge and Data Engineering, 11(5):798-805.
  10. Hipp, J., Myka, A., Wirth, R., and Güntzer, U. (1998). A new algorithm for faster mining of generalized association rules. In Zytkow, J. M. and Quafafou, M., editors, Proceedings of the 2nd European Symposium on Principles of Data Mining and Knowledge Discovery PKDD'98, pages 74-82.
  11. Huang, Y.-F. and Wu, C.-M. (2002). Mining generalized association rules using pruning techniques. In Proceedings of the 2002 IEEE International Conference on Data Mining (ICDM'02), pages 227-234, Washington, DC, USA. IEEE Computer Society.
  12. Srikant, R. and Agrawal, R. (1995). Mining generalized association rules. In Proceedings of the 21th International Conference on Very Large Data Bases VLDB'95, pages 407-419.
  13. Srikant, R. and Agrawal, R. (1997). Mining generalized association rules. Future Generation Computer Systems, 13(2/3):161-180.
  14. Sriphaew, K. and Theeramunkong, T. (2004). Fast algorithms for mining generalized frequent patterns of generalized association rules. IEICE Transactions on Information and Systems, 87(3):761-770.
  15. Tan, P.-N., Kumar, V., and Srivastava, J. (2004). Selecting the right objective measure for association analysis. Information Systems, 29(4):293-313.
  16. Weber, I. (1998). On pruning strategies for discovery of generalized and quantitative association rules. In Bing, I. L., Hsu, W., and Ke, W., editors, Proceedings Knowledge Discovery and Data Mining Workshop Pricai'98. 8 pp.
  17. Yen, S.-J. and Chen, A. L. P. (2001). A graph-based approach for discovering various types of association rules. IEEE Transactions on Knowledge and Data Engineering, 13(5):839-845.
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Paper Citation


in Harvard Style

Oliveira de Carvalho V., Oliveira Rezende S. and de Castro M. (2007). OBTAINING AND EVALUATING GENERALIZED ASSOCIATION RULES . In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-89-4, pages 310-315. DOI: 10.5220/0002367703100315


in Bibtex Style

@conference{iceis07,
author={Veronica Oliveira de Carvalho and Solange Oliveira Rezende and Mário de Castro},
title={OBTAINING AND EVALUATING GENERALIZED ASSOCIATION RULES},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2007},
pages={310-315},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002367703100315},
isbn={978-972-8865-89-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - OBTAINING AND EVALUATING GENERALIZED ASSOCIATION RULES
SN - 978-972-8865-89-4
AU - Oliveira de Carvalho V.
AU - Oliveira Rezende S.
AU - de Castro M.
PY - 2007
SP - 310
EP - 315
DO - 10.5220/0002367703100315