Abstract
Association rule learning is a familiar technique for finding interesting relations that exist between variables in immense databases. Existing mining techniques which are available at present cannot pay attention to negative dependencies and considering only one evaluation criteria for measuring quantity of obtained rules. But for better results, data sets demanding for populating negative associations. To overcome the problem, proposing a technique called Bit Vector (BV) generation for mining quantitative association rules of bipolar (Positive and Negative) item sets together. Proposed system can reduce time complexity of finding recurrent item sets of bipolar association rules and provide more flexibility for finding the frequent item 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
Bibliography
R. Renesse, K. Birman, and W. Vogels, “Astrolabe: A robust and scalable technology for distributed systems monitoring, management, and data mining,” ACM Transactions on Computational System, volume. 21, No. 2, pp. 164–206, 2003.
J. Han and M. Kamber, “Data Mining Concepts and Techniques”, 2nd ed. Burlington, MA, USA: Morgan Kaufmann, 2006.
C. Zhang and S. Zhang, “Association rule mining: Models and algorithms,” in Lecture Notes Computer Science (LNAI 2307). Berlin/Heidelberg, Germany Springer-Verlag, 2002.
R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” in Proc. SIGMOD, 1993, pp. 207–216.
R. Agrawal and R. Srikant, “Fast algorithms for mining association rules,” in Proc. Int. Conf. Large Data Bases, 1994, pp. 487–499.
R. Srikant and R. Agrawal, “Mining quantitative association rules in large relational tables,” in Proc. ACM SIGMOD, 1996, pp. 1–12.
X. Yan, C. Zhang, and S. Zhang, “Genetic algorithm-based strategy for identifying association rules without specifying actual minimum support,” Expert Syst. Appl., vol. 36, no. 2, pp. 3066–3076, 2009.
J. Alcala-Fdez, N. Flugy-Pape, A. Bonarini, and F. Herrera, “Analysis of the effectiveness of the genetic algorithms based on extraction of association rules,” Fund. Inf., vol. 98, no. 1, pp. 1001–1014, 2010.
D. Goldberg,“Genetic Algorithms in Search, Optimization and Machine Learning”. Reading, MA, USA/White Plains, NY, USA: Addison- Wesley/Longman, 1989.
B. Alatas and E. Akin, “MODENAR: Multi-objective differential evolution algorithm for mining numeric association rules,” Appl. Soft Comput., vol. 8, no. 1, p. 646, 2008.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Koda, D.K., Vinod Babu, P. (2017). Bit Resultant Matrix for Mining Quantitative Association Rules of Bipolar Item Sets. In: Satapathy, S., Bhateja, V., Raju, K., Janakiramaiah, B. (eds) Computer Communication, Networking and Internet Security. Lecture Notes in Networks and Systems, vol 5. Springer, Singapore. https://doi.org/10.1007/978-981-10-3226-4_31
Download citation
DOI: https://doi.org/10.1007/978-981-10-3226-4_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-3225-7
Online ISBN: 978-981-10-3226-4
eBook Packages: EngineeringEngineering (R0)