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A node sets based fast and scalable frequent itemset algorithm for mining big data using map reduce paradigm

Author

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  • B. Sivaiah
  • R. Rajeswara Rao

Abstract

Big data is rapidly growing, making traditional tools inefficient for handling large amounts of data. Existing algorithms for frequent itemset mining struggle with scalability due to limitations in parallel processing power. In this paper, we proposed a fast and scalable frequent itemset mining (FSFIM) algorithm used to generate frequent item sets from huge data. Preorder coding (POC) trees and Nodeset data structures save half the memory of node-lists and N-lists. The FSFIM uses Cloudera's CDH Map Reduce framework. With a maximum speedup value of 1.85 when minimal support is set to 1, The experimental results reveal that FSFIM outperforms the state-of-the-art methods such as HBPFP, Mlib PFP, and Big FIM. Fast and scalable frequent itemset mining algorithm is more scalable and faster for mining frequent item sets from big data.

Suggested Citation

  • B. Sivaiah & R. Rajeswara Rao, 2024. "A node sets based fast and scalable frequent itemset algorithm for mining big data using map reduce paradigm," International Journal of Data Mining, Modelling and Management, Inderscience Enterprises Ltd, vol. 16(3), pages 326-343.
    • Handle: RePEc:ids:ijdmmm:v:16:y:2024:i:3:p:326-343
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