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Near Candidate-Less Apriori With Tidlists and Other Apriori Implementations

Author

Listed:
  • Mehmet Bicer

    (Graduate Center, CUNY, USA)

  • Daniel Indictor

    (Columbia University, USA)

  • Ryan Yang

    (Massachusetts Institute of Technology, USA)

  • Xiaowen Zhang

    (College of Staten Island, CUNY, USA)

Abstract

In this study we implemented four different versions of Apriori, namely, basic and basic multi-threaded, bloom filter, trie, and count-min sketch, and proposed a new algorithm – NCLAT (Near Candidate-Less Apriori with Tidlists). We compared the runtimes and max memory usages of our implementations among each other as well as with the runtime of Borgelt’s Apriori implementation in some of the cases. NCLAT implementation is more efficient than the other Apriori implementations that we know of in terms of the number of times the database is scanned, and the number of candidates generated. Unlike the original Apriori algorithm which scans the database for every level and creates all of the candidates in advance for each level, NCLAT scans the database only once and creates candidate itemsets only for level one but not afterwards. Thus the number of candidates created is equal to the number of unique items in the database.

Suggested Citation

  • Mehmet Bicer & Daniel Indictor & Ryan Yang & Xiaowen Zhang, 2022. "Near Candidate-Less Apriori With Tidlists and Other Apriori Implementations," International Journal of Applied Logistics (IJAL), IGI Global, vol. 12(1), pages 1-22, January.
    • Handle: RePEc:igg:jal000:v:12:y:2022:i:1:p:1-22
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