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Reliable Distributed Fuzzy Discretizer for Associative Classification of Big Data

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  • Hepzi Jeya Pushparani

    (Sarah Tucker College, Manonmaniam Sundaranar University, India)

  • Nancy Jasmine Goldena

    (Sarah Tucker College, Manonmaniam Sundaranar University, India)

Abstract

Data Mining is an essential task because the digital world creates huge data daily. Associative classification is one of the data mining task which is used to carry out classification of data, based on the demand of knowledge users. Most of the associative classification algorithms are not able to analyze the big data which are mostly continuous in nature. This leads to the interest of analyzing the existing discretization algorithms which converts continuous data into discrete values and the development of novel discretizer Reliable Distributed Fuzzy Discretizer for big data set. Many discretizers suffer the problem of over splitting the partitions. Our proposed method is implemented in distributed fuzzy environment and aims to avoid over splitting of partitions by introducing a novel stopping criteria. Proposed discretization method is compared with existing distributed fuzzy partitioning method and achieved good accuracy in the performance of associative classifiers.

Suggested Citation

  • Hepzi Jeya Pushparani & Nancy Jasmine Goldena, 2022. "Reliable Distributed Fuzzy Discretizer for Associative Classification of Big Data," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 12(1), pages 1-13, January.
    • Handle: RePEc:igg:jirr00:v:12:y:2022:i:1:p:1-13
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    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJIRR.289572
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    References listed on IDEAS

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    1. Neda Abdelhamid & Aladdin Ayesh & Fadi Thabtah & Samad Ahmadi & Wael Hadi, 2012. "MAC: A Multiclass Associative Classification Algorithm," Journal of Information & Knowledge Management (JIKM), World Scientific Publishing Co. Pte. Ltd., vol. 11(02), pages 1-10.
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