Abstract
Technological changes have occurred at an exponential rate in recent years leading to the generation of large amount of data in various sectors. Several database and data warehouse is built to store and manage the data. As we know the data which are relevant to us should be extracted from the database for our task. Earlier different mining approaches are proposed in which items are collected at same minimum support value. In this paper we propose a fuzzy data mining algorithm which generates the fuzzy association rules from time series data having different minimum support values. The temperature varying dataset is used to generate fuzzy rules. The proposed algorithm also predicts the variation of temperature. Experiments are also performed to get the desired result.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Aggrawal, R.: Mining association rules between sets of items inlarge database ACM
Stepnicka, M.: Time series analysis and prediction based on fuzzy rules and the fuzzy transform
Dr.Sivatsa S.K.: Inaccuracy minimization by partitioning fuzzy data sets—validation of an analytical methodology(IJCSIS). Int. J. Comput. Sci. Inf. Secur. 8(1), (2010)
Das, G.: Rule discovery from time series. In: Proceedings of the 4 the International Conference
Pongracz, R.: Application of fuzzy rule-based modeling technique to regional drought. J. Hydrol. 224, 100–114 (1999)
Mueen, A.A.: Exact primitives for time series data mining. University of California, Riverside (2012)
Zhu, Y.: High performance data mining in time series: techniques and case studies. New work University, New York (2004)
Herrera, Francisco: Learning the membership function contexts for mining fuzzy association rules by using genetic algorithms. Fuzzy Sets Syst. 160, 905–921 (2009)
Han, J.: Data Mining concepts and Techniques
Hong, T.P.: Mining association rules with multiple minimum supports. Int. J. Approximate reasoning. 3, 38–42 (2005)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer India
About this paper
Cite this paper
Rathi, R., Jain, V., Gautam, A.K. (2014). Inducing Fuzzy Association Rules with Multiple Minimum Supports for Time Series Data. In: Babu, B., et al. Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. Advances in Intelligent Systems and Computing, vol 236. Springer, New Delhi. https://doi.org/10.1007/978-81-322-1602-5_47
Download citation
DOI: https://doi.org/10.1007/978-81-322-1602-5_47
Published:
Publisher Name: Springer, New Delhi
Print ISBN: 978-81-322-1601-8
Online ISBN: 978-81-322-1602-5
eBook Packages: EngineeringEngineering (R0)