TY - GEN
T1 - A Parallel Incremental Frequent Itemsets Mining IFIN+: Improvement and Extensive Evaluation
AU - Huynh, Van Quoc
AU - Küng, Josef
AU - Dang, Tran Khanh
PY - 2019/2
Y1 - 2019/2
N2 - 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.
AB - 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.
UR - https://www.scopus.com/pages/publications/85062278385
U2 - 10.1007/978-3-662-58808-6_4
DO - 10.1007/978-3-662-58808-6_4
M3 - Conference proceedings
VL - 11390
T3 - Lecture Notes in Computer Science (LNCS)
SP - 78
EP - 106
BT - Transactions on Large-Scale Data- and Knowledge-Centered Systems XLI. Special Issue on Data and Security Engineering
A2 - Dang, Tran Khanh
A2 - Wagner, Roland
A2 - Hameurlain, Abdelkader
PB - Springer
ER -