Van Quoc Huynh, Josef Küng, Tran Khanh Dang,
"A Parallel Incremental Frequent Itemsets Mining IFIN+: Improvement and Extensive Evaluation"
: Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI. Special Issue on Data and Security Engineering, Serie Lecture Notes in Computer Science (LNCS), Vol. 11390, Springer, Seite(n) 78-106, 2-2019
Original Titel:
A Parallel Incremental Frequent Itemsets Mining IFIN+: Improvement and Extensive Evaluation
Sprache des Titels:
Englisch
Original Buchtitel:
Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI. Special Issue on Data and Security Engineering
Original Kurzfassung:
In this paper, we propose a shared-memory parallelization solution for the Frequent Itemsets Mining algorithm IFIN, called IFIN+. The motivation for our work is that commodity processors, nowadays, are enhanced with many physical computational units, and exploiting full advantage of this is a potential solution to improve computational performance in single-machine environments. The portions in the serial version are improved in means which increases efficiency and computational independence for convenience in designing parallel computation with Work-Pool model, be known as a good model for load balance. We conducted extensive experiments on both synthetic and real datasets to evaluate IFIN+ against its serial version IFIN, the well-known algorithm FP-Growth and other two state-of-the-art ones, FIN and PrePost+. The experimental results show that the running time of IFIN+ is the most efficient, especially in the case of mining at different support thresholds within the same running session. Compare to its serial version, IFIN+ performance is improved significantly.
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