NARFO* ALGORITHM - Optimizing the Process of Obtaining Non-redundant and Generalized Semantic Association Rules

Rafael Garcia Miani, Cristiane Akemi Yaguinuma, Marilde Terezinha Prado Santos, Vinícius Ramos Toledo Ferraz

2010

Abstract

This paper proposes the NARFO* algorithm, an algorithm for mining non-redundant and generalized association rules based on fuzzy ontologies. The main contribution of this work is to optimize the process of obtaining non-redundant and generalized semantic association rules by introducing the minGen (Minimal Generalization) parameter in the latest version of NARFO algorithm. This parameter acts on generalize rules, especially the ones with low minimum support, preserving their semantic and eliminating redundancy, thus reducing considerably the amount of generated rules. Experiments showed that NARFO* produces semantic rules, without redundancy, obtaining 68,75% and 55,54% of reduction in comparison with XSSDM algorithm and NARFO algorithm, respectively.

References

  1. Bayardo, J. R. J. (1998). Efficiently mining long patterns from databases. In Proceedings of the 1998 Annual Conference on Management of Data. Seattle, USA. 2- 4 June 1998.
  2. Brisson, L., Collard, M., Pasquier, N. (2005). Improving Knowledge Discovery Process Using Ontologies. In International Workshop on Mining Complex Data. Houston, USA. 27-30 November 2005.
  3. Chen, G., Wei, Q., Kerre, E. E. (2000). Fuzzy Data Mining: Discovery of Fuzzy Generalized Association Rules. In: G. Bordogna and G. Pasi, eds. Recent Issues on Fuzzy Databases. Wurzburg: Physica-Verlag. 45- 66.
  4. Chen, X., Zhou, X., Scherl, R. B., Geller, J. (2003). Using an Interesting Ontology for Improved Support in Rule Mining. In 5th InternationalConference on Data Warehousing and Knowledge Discovery. Prague, Czech Republic. 3-5 September 2003.
  5. Escovar, E. L. G., Biajiz, M., Vieira, M. T. P. (2005). SSDM: A Semantically Similar Data Mining Algorithm. In 20th Brazilian Symposium of Databases. Uberlândia, Brazil. 3-7 October 2005.
  6. Escovar, E. L. G., Yaguinuma, C. A., Biajiz, M. Using Fuzzy Ontologies to Extend Semantically Similar Data Mining. In 21th Brazilian Symposium of Databases. Florianópolis, Brazil. 16-20 October 2006.
  7. Farzanyar, Z., Kangavari, M., Hashemi, S. (2006). A New Algorithm for Mining Fuzzy Association Rules in the Large Databases Based on Ontology. In Sixth IEEE International Conference on Data Mining - Workshops. Hong Kong, China. 18-22 December 2006.
  8. Han, J., Fu, Y. (1999). Mining Multiple-Level Association Rules in Large Databases. IEEE Transactions on Knowledge and Data Engeneering, 11(5), 798-805.
  9. Hou, X., Gu, J., Shen, X., Yan, W. (2005). Application of Data Mining in Fault Diagnosis Based on Ontology. In 3th Third International Conference on Information Technology and Applications. Sydney, Australia. 4-7 July 2005.
  10. Kunkle, D. Zhang, D. H., Cooperman, G. (2008). Mining Generalized Frequent Itemsets and Generalized Association Rules Without Redundancy. Journal of Computer Science and Technology, 23(1), 77-102.
  11. Miani, R. G., Yaguinuma, C. A., Santos, M. T. P., Biajiz, M. (2009). NARFO Algorithm: Mining Nonredundant and Generalized Association Rules Based on Fuzzy Ontologies. In 11 International Conference on Enterprise Information Systems. Milan, Italy. 6-10 May 2009.
  12. Oliveira, V. C., Rezende, S. O., Castro, M. (2007). Evaluating Generalized Association Rules Through Objective Measures. In Proceedings of 25th International Multi-Conference on Artificial Intelligence and Applications . Innsbruck, Austria. 12- 14 February 2007.
  13. Srikant, R., Agrawal, R., (1995). Mining Generalized Association Rules. In Proceedings of the International Conference of Very Large Data Bases. Zurich, Suíça. 11-15 September 1995.
Download


Paper Citation


in Harvard Style

Garcia Miani R., Akemi Yaguinuma C., Terezinha Prado Santos M. and Ramos Toledo Ferraz V. (2010). NARFO* ALGORITHM - Optimizing the Process of Obtaining Non-redundant and Generalized Semantic Association Rules . In Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8425-05-8, pages 320-325. DOI: 10.5220/0002968803200325


in Bibtex Style

@conference{iceis10,
author={Rafael Garcia Miani and Cristiane Akemi Yaguinuma and Marilde Terezinha Prado Santos and Vinícius Ramos Toledo Ferraz},
title={NARFO* ALGORITHM - Optimizing the Process of Obtaining Non-redundant and Generalized Semantic Association Rules},
booktitle={Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2010},
pages={320-325},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002968803200325},
isbn={978-989-8425-05-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 12th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - NARFO* ALGORITHM - Optimizing the Process of Obtaining Non-redundant and Generalized Semantic Association Rules
SN - 978-989-8425-05-8
AU - Garcia Miani R.
AU - Akemi Yaguinuma C.
AU - Terezinha Prado Santos M.
AU - Ramos Toledo Ferraz V.
PY - 2010
SP - 320
EP - 325
DO - 10.5220/0002968803200325