Abstract
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.
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Phan, H. (2019). An Efficient Mining Algorithm of Closed Frequent Itemsets on Multi-core Processor. In: Li, J., Wang, S., Qin, S., Li, X., Wang, S. (eds) Advanced Data Mining and Applications. ADMA 2019. Lecture Notes in Computer Science(), vol 11888. Springer, Cham. https://doi.org/10.1007/978-3-030-35231-8_8
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DOI: https://doi.org/10.1007/978-3-030-35231-8_8
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