An Efficient Mining Algorithm of Closed Frequent Itemsets on Multi-core Processor

  • Huan PhanEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11888)


In this paper, we improved a sequential NOV-CFI algorithm mining closed frequent itemsets in transaction databases, called SEQ-CFI and consisting of three phases: the first phase, quickly detect a Kernel_COOC array of co-occurrences and occurrences of kernel item in at least one transaction; the second phase, we built the list of nLOOC-Tree base on the Kernel_COOC and a binary matrix of dataset (self-reduced search space); the last phase, the algorithm is a fast mining closed frequent itemsets base on nLOOC-Tree. The next step, we develop a sequential algorithm for mining closed frequent itemsets and thus parallelize the sequential algorithm to effectively demonstrate the multi-core processor, called NPA-CFI. The experimental results show that the proposed algorithms perform better than other existing algorithms, as well as to expand the parallel NPA-CFI algorithm on distributed computing systems such as Hadoop, Spark.


Co-occurrence items Closed frequent itemsets Multi-core processor Parallel NPA-CFI algorithm 


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Authors and Affiliations

  1. 1.Division of ITUniversity of Social Sciences and Humanities – VNU-HCMCHo Chi Minh CityVietnam
  2. 2.Faculty of Mathematics and Computer ScienceUniversity of Science – VNU-HCMCHo Chi Minh CityVietnam

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