Abstract
Association rules (AR) are a class of patterns which describe regularities in a set of transactions. When items of transactions are organized in a taxonomy, AR can be associated with a level of the taxonomy since they contain only items at that level. A drawback of multiple level AR mining is represented by the generation of redundant rules which do not add further information to that expressed by other rules. In this paper, a method for the discovery of non-redundant multiple level AR is proposed. It follows the usual two-stepped procedure for AR mining and it prunes redundancies in each step. In the first step, redundancies are removed by resorting to the notion of multiple level closed frequent itemsets, while in the second step, pruning is based on an extension of the notion of minimal rules. The proposed technique has been applied to a real case of analysis of textual data. An empirical comparison with the Apriori algorithm proves the advantages of the proposed method in terms of both time-performance and redundancy reduction.
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
Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proceedings of the 20th Int’l Conference on Very Large Data Bases, pp. 478–499 (1994)
Agrawal, R., Srikant, R.: Mining Generalized Association Rules. In: Proc. of the 21st Int’l Conf. on Very Large Databases, pp. 407–419 (1995)
Ashrafi, M., Taniar, D., Smith, K.: Redundant Association Rules Reduction Techniques. In: Zhang, S., Jarvis, R.A. (eds.) AI 2005. LNCS, vol. 3809, pp. 254–263. Springer, Heidelberg (2005)
Baralis, E., Psaila, G.: Designing Templates for Mining Association Rules. J. Intell. Inf. Syst. 9(1), 7–32 (1997)
Bastide, Y., Pasquier, N., Taouil, R., Stumme, G., Lakhal, L.: Mining minimal non-redundant association rules using frequent closed itemsets. In: Palamidessi, C., Moniz Pereira, L., Lloyd, J.W., Dahl, V., Furbach, U., Kerber, M., Lau, K.-K., Sagiv, Y., Stuckey, P.J. (eds.) CL 2000. LNCS, vol. 1861, pp. 972–986. Springer, Heidelberg (2000)
Davey, B.A., Priestley, H.A.: Lattices Theory. Introduction to Lattices and Order, 4th edn. Cambridge University Press, Cambridge (1994)
Han, J., Fu, Y.: Mining Multiple-Level Association Rules in Large Databases. IEEE Trans. Knowl. Data Eng. 11(5), 798–804 (1999)
Hernandez Palancar, J., Fraxedas Tormo, O., Feston Cardenas, J., Hernandez-Leon, R.: Distributed and Shared Memory Algorithm for Parallel Mining of Association Rules. In: Perner, P. (ed.) MLDM 2007. LNCS (LNAI), vol. 4571, pp. 349–363. Springer, Heidelberg (2007)
Hilderman, R.J., Hamilton, H.J.: Knowledge Discovery and Interest Measures. Kluwer Academic, Boston (2002)
Kanda, K., Haraguchi, M., Okubo, Y.: Constructing Approximate Informative Basis of Association Rules. Discovery Science, 141–154 (2001)
Klemettinen, M., Mannila, H., Ronkainen, P., Toivonen, H., Verkamo, A.I.: Finding Interesting Rules from Large Sets of Discovered Association Rules. In: Proc. of the 3rd Int. Conf. on Information and Knowledge Management, pp. 401–407 (1994)
Kotsiantis, S., Kanellopoulos, D.: Association Rules Mining: A Recent Overview. GESTS Int. Trans. on Computer Science and Eng. 32(1), 71–82 (2006)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering frequent closed itemsets for association rules. In: Proc. of 7th Int. Conf. on DB Theory, pp. 398–416 (1999)
Sharma, L.K., Vyas, O.P., Tiwary, U.S., Vyas, R.: A Novel Approach of Multilevel Positive and Negative Association Rule Mining for Spatial Databases. In: Perner, P., Imiya, A. (eds.) MLDM 2005. LNCS (LNAI), vol. 3587, pp. 620–629. Springer, Heidelberg (2005)
Sriphaew, K., Theeramunkong, T.: Mining Generalized Closed Frequent Itemsets of Generalized Association Rules. In: Proc. of 7th Int. Conf. on Knowledge-Based Intell. Inf. and Eng. Systems, pp. 476–484 (2003)
Zaki, M.J.: Generating non-redundant association rules. In: Proc. of The Ninth ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining, pp. 34–43 (2000)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Loglisci, C., Malerba, D. (2009). Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2009. Lecture Notes in Computer Science(), vol 5632. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-03070-3_19
Download citation
DOI: https://doi.org/10.1007/978-3-642-03070-3_19
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-03069-7
Online ISBN: 978-3-642-03070-3
eBook Packages: Computer ScienceComputer Science (R0)