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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 236))

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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.

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Correspondence to Rakesh Rathi .

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© 2014 Springer India

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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

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  • DOI: https://doi.org/10.1007/978-81-322-1602-5_47

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  • Publisher Name: Springer, New Delhi

  • Print ISBN: 978-81-322-1601-8

  • Online ISBN: 978-81-322-1602-5

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