Abstract
Typical association rules consider only items enumerated in transactions. Such rules are referred to as positive association rules. Negative association rules also consider the same items, but in addition consider negated items (i. e. absent from transactions). Negative association rules are useful in market-basket analysis to identify products that conflict with each other or products that complement each other. They are also very convenient for associative classifiers, classifiers that build their classification model based on association rules. Indeed, mining for such rules necessitates the examination of an exponentially large search space. Despite their usefulness, very few algorithms to mine them have been proposed to date. In this paper, an algorithm based on FP-tree is presented to discover negative association rules.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agrawal R, Imielinski T, Swami A. Mining Association Rules between Sets of Items in Large Databases.Proc of ACM SIGMOD Int Conf Management of Date, Washington D C. 1993. 207–216.
Kamber H J.MData Mining: Concepts and Techniques. Beijing: High Education Press, 2001.
Goethals B. Survey on Frequent Pattern Mining. Helsinki Institute for Information Technology. Technical Report, 2003.
Wang Xiao-feng, Wan Tian-ran, Zhao Yue. An Effective Top to Down Data Mining Method for Long Frequents.Journal of Computer Research and Development, 2004,41 (1): 148–155 (Ch).
Han Jia-wei, Pei Jian, Yin Yi-wen. Mining Frequent Patterns without Candidate Generation.Proc of 2000ACM SIGMOD International Conference on Management of Data. Dallas, TX, April 2000. 1–12.
Zhu Yu-quan, Sun Zhi-hui, Ji Xiao-jun. Increamental Updating Algorithm Based on Frequent Pattern Tree for Mining Association Rules.Chinese Journal of Computers, 2003,26 (1): 91–96 (Ch).
Zhu Yu-quan, Sun Zhi-hui, Zhao Zhuan-shen, Fast Updating Frequent Item Sets.Journal of Computer Research and Development, 2003,40(1): 94–99 (Ch).
Savasere A, Omiecinski E, Navathe S. Mining for Strong Negative Rules for Statistically Dependent Items.Proc of 18th International Conference on Data Engineering (ICDE'02). San Jose, CA, February 2002, 442–449.
Wu Xin-dong, Zhang Cheng-qi, Zhang Shi-chao. Mining both Positive and Negative Association Rules.Proc of the 19th International Conference on Machine Learning. San Mateo: Morgan Kaufmann Publishers, 2002. 658–665.
Maria-Luiza A, Osmar R Z. Mining Positive and Negative Association Rules: An Approach for Confined Rules.Proc of 8th European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD 04). Pisa, Italy, September, 2004, LNCS3202: 27–38.
Author information
Authors and Affiliations
Additional information
Foundation item: Supported by the National Natural Science Foundation of China (70371015) and the Science Foundation of Jiangsu University (04KJD001)
Biography: ZHU Yu-quan (1965-), male, Associate professor, Ph. D., research direction: data mining and knowledge discovery.
Rights and permissions
About this article
Cite this article
Yu-quan, Z., He-blao, Y., Yu-qing, S. et al. Research on algorithm for mining negative association rules based on frequent pattern tree. Wuhan Univ. J. Nat. Sci. 11, 37–41 (2006). https://doi.org/10.1007/BF02831700
Received:
Issue Date:
DOI: https://doi.org/10.1007/BF02831700