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Efficient Handling of High-Dimensional Data in Distributed Association Rule Mining

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  • Hitesh Ninama

Abstract

High-dimensional data poses significant challenges in Distributed Association Rule Mining (DARM), including increased computational complexity and execution time. This paper proposes an integrated methodology combining Principal Component Analysis (PCA) for dimensionality reduction, FP-tree construction, and parallel processing using frameworks like MapReduce and Apache Spark. Experiments on synthetic datasets demonstrate that the proposed approach significantly reduces execution time and simplifies the rule set while retaining meaningful patterns. These findings highlight the effectiveness of the methodology in improving the scalability and efficiency of DARM.

Suggested Citation

  • Hitesh Ninama, 2018. "Efficient Handling of High-Dimensional Data in Distributed Association Rule Mining," International Journal of Scientific Research in Computer Science, Engineering and Information Technology, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 3(3), pages 2178-2186, April.
    • Handle: RePEc:jbh:ijsrcs:v3:y2018:i3:id:hcseit1820115
    Note: Article URL: https://ijsrcseit.com/CSEIT1820115
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