Abstract
In this paper, we tackled the problem of generation of rare classification rules. Our work is motivated by the search of an effective algorithm allowing the extraction of rare classification rules by avoiding the generation of a large number of patterns at reduced time. Within this framework we are interested in rules of the form a 1 ∧ a 2… ∧ a n ⇒b which allow us to propose a new approach based on genetic algorithms principle. This approach allows obtaining frequent and rare rules while avoiding making a breadth search. We describe our method and provide a comparative study of three versions of our method on standard benchmark data sets.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zaiane, O., Antonie, M.: On pruning and tuning rules for associative classifiers. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3683, pp. 966–973. Springer, Heidelberg (2005)
Xiaoxin Yin, J.H.: CPAR: Classification based on Predictive Association Rules. In: Proceedings of the SDM, San Francisco, CA, pp. 369–376 (2003)
Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. Knowledge Discovery and Data Mining, 80–86 (1998)
Antonie, M., Zaiane, O.: Text Document Categorization by Term Association. In: Proceedings of the IEEE International Conference on Data Mining (ICDM 2002), Maebashi City, Japan, pp. 19–26 (2002)
Antonie, M., Zaiane, O.: Classifying Text Documents by Associating Terms with Text Categories. In: Proceedings of the Thirteenth Austral-Asian Database Conference (ADC 2002), Melbourne, Australia (2002)
Bouzouita, I., Elloumi, S., Yahia, S.B.: GARC: A new associative classification approach. In: Tjoa, A.M., Trujillo, J. (eds.) DaWaK 2006. LNCS, vol. 4081, pp. 554–565. Springer, Heidelberg (2006)
Bouzouita, I., Elloumi, S.: Integrated generic association rules based classifier. In: Wagner, R., Revell, N., Pernul, G. (eds.) DEXA 2007. LNCS, vol. 4653, pp. 514–518. Springer, Heidelberg (2007)
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Bocca, J.B., Jarke, M., Zaniolo, C. (eds.) Proceedings of the 20th Intl. Conference on Very Large Databases, Santiago, Chile, pp. 478–499 (1994)
Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proceedings of IEEE International Conference on Data Mining (ICDM 2001), pp. 369–376. IEEE Computer Society, San Jose (2001)
Wang, J., Karypis, G.: HARMONY: Efficiently mining the best rules for classification. In: Proceedings of the International Conference of Data Mining, pp. 205–216 (2005)
Quinlan, J., Cameron-Jones, R.: FOIL: A midterm report. In: Proceedings of European Conference on Machine Learning, Vienna, Austria, pp. 3–20 (1993)
Bouzouita, I., Michel Liquire, S.E.: Afortiori: an associative classification approach based on covering set method. In: International Conference Of Formal Concept Analysis ICFCA 2009. Darmstadt University of Applied Sciences, Germany (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Bouzouita, I., Liquiere, M., Elloumi, S., Jaoua, A. (2011). A Comparative Study of a New Associative Classification Approach for Mining Rare and Frequent Classification Rules. In: Kim, Th., Adeli, H., Robles, R.J., Balitanas, M. (eds) Information Security and Assurance. ISA 2011. Communications in Computer and Information Science, vol 200. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23141-4_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-23141-4_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23140-7
Online ISBN: 978-3-642-23141-4
eBook Packages: Computer ScienceComputer Science (R0)