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Huynh HM, Nguyen LT, Vo B, Oplatková ZK, Fournier-Viger P, Yun U. An efficient parallel algorithm for mining weighted clickstream patterns. Inf Sci (N Y) 2022. [DOI: 10.1016/j.ins.2021.08.070] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
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Le T, Nguyen TL, Huynh B, Nguyen H, Hong TP, Snasel V. Mining colossal patterns with length constraints. APPL INTELL 2021. [DOI: 10.1007/s10489-021-02357-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
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Mining frequent weighted closed itemsets using the WN-list structure and an early pruning strategy. APPL INTELL 2020. [DOI: 10.1007/s10489-020-01899-7] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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Francia M, Golfarelli M, Rizzi S. Summarization and visualization of multi-level and multi-dimensional itemsets. Inf Sci (N Y) 2020. [DOI: 10.1016/j.ins.2020.02.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
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Abstract
AbstractThis paper explores five pattern mining problems and proposes a new distributed framework called DT-DPM: Decomposition Transaction for Distributed Pattern Mining. DT-DPM addresses the limitations of the existing pattern mining problems by reducing the enumeration search space. Thus, it derives the relevant patterns by studying the different correlation among the transactions. It first decomposes the set of transactions into several clusters of different sizes, and then explores heterogeneous architectures, including MapReduce, single CPU, and multi CPU, based on the densities of each subset of transactions. To evaluate the DT-DPM framework, extensive experiments were carried out by solving five pattern mining problems (FIM: Frequent Itemset Mining, WIM: Weighted Itemset Mining, UIM: Uncertain Itemset Mining, HUIM: High Utility Itemset Mining, and SPM: Sequential Pattern Mining). Experimental results reveal that by using DT-DPM, the scalability of the pattern mining algorithms was improved on large databases. Results also reveal that DT-DPM outperforms the baseline parallel pattern mining algorithms on big databases.
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