Generating a Condensed Representation for Positive and Negative Association Rules

A Condensed Representation for Association Rules




Association rules, Generator itemsets, Closed itemsets, Minimal infrequent itemsets


Given a large collection of transactions containing items, a basic common association rules problem is the huge size of the extracted rule set. Pruning uninteresting and redundant association rules is a promising approach to solve this problem. In this paper, we propose a Condensed Representation for Positive and Negative Association Rules representing non-redundant rules for both exact and approximate association rules based on the sets of frequent generator itemsets, frequent closed itemsets, maximal frequent itemsets, and minimal infrequent itemsets in database B. Experiments on dense (highly-correlated) databases show a significant reduction of the size of extracted association rule set in database B.


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R. Agrawal, and R. Srikant, "Fast Algorithms for Mining Association Rules". In Proceedings of 20th VLDB Conference, pp. 487–499. Santiago, Chile (1994).

Y. Bastide, N. Pasquier, R. Taouil, G. Stumme, and L. Lakhal, "Mining Minimal Non-Redundant Association Rules using Frequent Closed Itemsets". In CL’2000 international conference Computational Logic, pp. 972–986 (2000).

P. Bemarisika, and A. Totohasina, "An Informative Base of Positive and Negative Association Rules on Big Data". In Proc. of BigData, pp. 2428–2437 (2019).

L. Cao, X. Dong, and Z. Zheng, "E-NSP: Efficient negative sequential pattern mining". In Artificial Intelligence, pp. 156–182 (2016).

X. Dong, H. Hao, L. Zhao, and T. Xu, "An efficient method for pruning redundant negative and positive association rules". In NEUCOM 2018. (2018).

X. Dong, G. Yongshun, and L. Cao, "F-NSP+: A Fast Negative Sequential Patterns Mining Method with Self-adaption Data Storage Strategy". Pattern Rec.(2018).

D. Feno, J. Diatta, and A. Totohasina, "Galois Lattices and Based forMGK-valid Association Rules". In Ben Yahia et al. (Eds.), CLA 2006, pp. 186–197, (2006).

B. Ganter, and R. Wille, "Formal concept analysis: Mathematical foundations". In Springer Verlag (1999).

R. Gras, J-C. Régnier, C. Marinica, and F. Guillet, "L’ASI, Méthode exploratoire et confirmatoire recherche de causalités". In Cépaduditions, pp. 11–40 (2013).

J.L. Guigues, and V. Duquenne, "Familles minimales d’implications informatives résultant d’un tableau de donnés binaires". Maths et Sci. Humaines, 5–18 (1986).

T. Guyet, and R. Quiniou, "NegPSpan: efficient extraction of negative sequential patterns with embedding constraints". (2018).

M. Mannila, and H. Toivonen, "Levelwise Search and Borders of Theories in Knowledge Discovery". In Data Mining Knowledge Discovery, pp. 241–258 (1997).

N. Pasquier, R. Taouil, and Y. Bastide, G. Stumme, and L. Lakhal, "Generating a condensed representation for association rules". In J. of Intell. Info. Syst., pp. 29–60 (2005).

N. Pasquier, "Frequent Closed Itemsets Based Condensed Representations for Association Rules". In Tech. for Eff. Knowl. Extraction, pp. 248–273 (2009).

T. Xu, T. Li, and X. Dong, "Efficient High Utility Negative Sequential Patterns Mining in Smart Campusy". In IEEE Access, pp. 23839–23846, (2018).

Y. Xu, Y. Li, and G. Shaw, "Reliable representations for association rules". In Data and Knowledge Engineering, pp. 555–575 (2011).