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A parallel approach for high utility-based frequent pattern mining in a big data environment

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Abstract

In recent day’s social media, smartphones, mobile apps, and the internet of things generate huge amounts of data every second. This data is structured, unstructured, or semi-structured and available in various formats. Therefore, traditional approaches are not sufficient to handle such kind of data effectively. On the other hand, high utility based frequent patterns are also essential to make effective decisions, and its demand is also increasing in recent years. Many strategies have been proposed for high utility based pattern generation. But these methods are limited to data size and operate on standalone systems. To address this issue, we have proposed a parallel procedure for high utility based frequent pattern mining. The proposed approach can handle huge amounts of data, i.e., big data. It can be used in various real-life applications and benefited citizens. The proposed technique is implemented in cluster-node architecture using Spark. The results are validated on various real-time data sets and found that the proposed strategy is more efficient than traditional methods in terms of execution time. The results of the proposed work will provide solutions to real-time issues such as health care, education, transport, and so on.

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Notes

  1. http://www.philippe-fournier-viger.com/spmf/.

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Authors and Affiliations

  1. Department of Computer Science, Central University of Rajasthan, Ajmer, India

    Krishna Kumar Mohbey & Sunil Kumar

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  1. Krishna Kumar Mohbey
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  2. Sunil Kumar
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Correspondence to Krishna Kumar Mohbey.

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Mohbey, K.K., Kumar, S. A parallel approach for high utility-based frequent pattern mining in a big data environment. Iran J Comput Sci 4, 195–200 (2021). https://doi.org/10.1007/s42044-021-00083-5

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  • DOI: https://doi.org/10.1007/s42044-021-00083-5

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