Mining Generalized Association Rules using Fuzzy Ontologies with Context-based Similarity
Rodrigo Moura Juvenil Ayres, Marilde Terezinha Prado Santos
2012
Abstract
In crisp contexts taxonomies are used in different steps of the mining process. When the objective is the generalization they are used, manly, in the pre-processing or post-processing stages. On the other hand, in fuzzy contexts, fuzzy taxonomies are used, mainly, in the pre-processing step, during the generation of extended transactions. A great problem of such transactions is related to the generation of huge amount of candidates and rules. Beyond that, the inclusion of ancestors in the same ends up generating problems of redundancy. Besides, it is possible to see that many works have directed efforts for the question of mining fuzzy rules, exploring linguistic terms, but few approaches have proposed new steps of the mining process. In this sense, this paper propose the Context FOntGAR algorithm, a new algorithm for mining generalized association rules under all levels of fuzzy ontologies composed by specialization/generalization degrees varying in the interval [0,1]. In order to obtain more semantic enrichment, the rules may be composed by similarity relations, which are represented at the fuzzy ontologies in different contexts. In this work the generalization is done during the post-processing step. Other relevant points are the specification of a generalization approach; including a grouping rules treatment, and an efficient way of calculating both support and confidence of generalized rules during this step.
References
- Agrawal, R., T. Imielinski, et al. (1993). Mining association rules between sets of items in large databases, Washington, DC, USA, ACM.
- Agrawal, R. and R. Srikant (1994). Fast algorithms for mining association rules. Conference on Very Large Databases (VLDB). Santiago, Chile, Morgan Kaufmann Publischers Inc.: 487-499.
- Cai, C. H., Ada, et al. (1998). Mining Association Rules with Weighted Items. International Database Engineering and Application Symposium.
- Carvalho, V. O. D., S. O. Rezende, et al. (2007). Obtaining and evaluating generalized association rules. 9th International Conference on Enterprise Information Systems, ICEIS 2007, Funchal, Madeira; 12 June 2007 through 16 June 2007.
- Cerri, M. J., C. Yaguinuma, et al. (2010). UFOCoRe: Exploring Fuzzy Relations According to Specifics Contexts. International Conference on Software Engineering & Knowledge Engineering (SEKE 2010). San Francisco Bay, USA: 529-534.
- Chen, G. and Q. Wei (2002). "Fuzzy association rules and the extended mining algorithms." Information Sciences - Informatics and Computer Science: An International Journal 147(1-4): 201-228.
- Escovar, E. L. G., M. Biajiz, et al. (2005). "SSDM: A Semantically Similar Data Mining Algorithm." 20 Brazilian Symposium of Databases.
- Escovar, E. L. G., C. A. Yaguinuma, et al. (2006). Using Fuzzy Ontologies to Extend Semantically Similar Data Mining. 21 Brazilian Symposium on Databases. Florianópolis, Brazil: 16-30.
- Hong, T. P., K. Y. Lin, et al. (2003). "Fuzzy data mining for interesting generalized association rules." Fuzzy Sets and Systems 138(2): 255-269.
- Hung-Pin, C., T. Yi-Tsung, et al. (2006). A Cluster-Based Method for Mining Generalized Fuzzy Association Rules. Innovative Computing, Information and Control, 2006. ICICIC 7806. First International Conference on.
- Jiawei Han and Y. Fu (1995). Discovery of Multiple-Level Association Rules from Large Databases. 21º VLDB Conference. Zurich, Switzerland: 420-431.
- Keon-Myung, L. (2001). Mining generalized fuzzy quantitative association rules with fuzzy generalization hierarchies. IFSA World Congress and 20th NAFIPS International Conference, 2001. Joint 9th.
- Lee, Y.-C., T.-P. Hong, et al. (2008). "Multi-level fuzzy mining with multiple minimum supports." Expert Systems with Applications: An International Journal 34(1): 459-468.
- Mahmoudi, E. V., E. Sabetnia, et al. (2011). Multi-level Fuzzy Association Rules Mining via Determining Minimum Supports and Membership Functions. Intelligent Systems, Second International Conference on Modelling and Simulation (ISMS), 2011.
- Miani, R. G., C. A. Yaguinuma, et al. (2009). NARFO Algorithm: Mining Non-redundant and Generalized Association Rules Based on Fuzzy Ontologies. Enterprise Information Systems. J. Filipe and J. Cordeiro, Springer Berlin Heidelberg. 24: 415-426.
- Smith, M. K., C. Welt, et al. (2004). "W3C Proposed Recomendation: OWL Web Ontology Language Guide." Retrieved 2 dezembro, 2010, from
- Srikant, R. and R. Agrawal (1995). Mining Generalized Association Rules. Proceedings of the 21th International Conference on Very Large Data Bases, Morgan Kaufmann Publishers Inc.
- Vo, B. and B. Le (2009). "Fast Algorithm for Mining Generalized Association Rules." International Journal of Database Theory and Application 2(3): 1-12.
- Wei, Q. and G. Chen (1999). Mining generalized association rules with fuzzy taxonomic structures. Fuzzy Information Processing Society, 1999. NAFIPS. 18th International Conference of the North American.
- Wen-Yang, L., T. Ming-Cheng, et al. (2010). Updating generalized association rules with evolving fuzzy taxonomies. IEEE International Conference on Fuzzy Systems (FUZZ), 2010.
- Wu, C.-M. and Y.-F. Huang (2011). "Generalized association rule mining using an efficient data structure." Expert Systems with Applications 38(6): 7277-7290.
- Zadeh, L. A. (1965). "Fuzzy sets." Information and Control 8(3): 338-353.
Paper Citation
in Harvard Style
Moura Juvenil Ayres R. and Terezinha Prado Santos M. (2012). Mining Generalized Association Rules using Fuzzy Ontologies with Context-based Similarity . In Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS, ISBN 978-989-8565-10-5, pages 74-83. DOI: 10.5220/0004011300740083
in Bibtex Style
@conference{iceis12,
author={Rodrigo Moura Juvenil Ayres and Marilde Terezinha Prado Santos},
title={Mining Generalized Association Rules using Fuzzy Ontologies with Context-based Similarity},
booktitle={Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,},
year={2012},
pages={74-83},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004011300740083},
isbn={978-989-8565-10-5},
}
in EndNote Style
TY - CONF
JO - Proceedings of the 14th International Conference on Enterprise Information Systems - Volume 1: ICEIS,
TI - Mining Generalized Association Rules using Fuzzy Ontologies with Context-based Similarity
SN - 978-989-8565-10-5
AU - Moura Juvenil Ayres R.
AU - Terezinha Prado Santos M.
PY - 2012
SP - 74
EP - 83
DO - 10.5220/0004011300740083