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Frequent Itemset Mining in Large Datasets a Survey

Author

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  • Amrit Pal

    (Indian Institute of Information Technology, Allahabad, India)

  • Manish Kumar

    (Indian Institute of Information Technology, Allahabad, India)

Abstract

Frequent Itemset Mining is a well-known area in data mining. Most of the techniques available for frequent itemset mining requires complete information about the data which can result in generation of the association rules. The amount of data is increasing day by day taking form of BigData, which require changes in the algorithms for working on such large-scale data. Parallel implementation of the mining techniques can provide solutions to this problem. In this paper a survey of frequent itemset mining techniques is done which can be used in a parallel environment. Programming models like Map Reduce provides efficient architecture for working with BigData, paper also provides information about issues and feasibility about technique to be implemented in such environment.

Suggested Citation

  • Amrit Pal & Manish Kumar, 2017. "Frequent Itemset Mining in Large Datasets a Survey," International Journal of Information Retrieval Research (IJIRR), IGI Global, vol. 7(4), pages 37-49, October.
    • Handle: RePEc:igg:jirr00:v:7:y:2017:i:4:p:37-49
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