@article{scholars11087,
            year = {2019},
       publisher = {Springer Netherlands},
         journal = {Artificial Intelligence Review},
           pages = {2603--2621},
            note = {cited By 56},
          volume = {52},
          number = {4},
             doi = {10.1007/s10462-018-9629-z},
           title = {Algorithms for frequent itemset mining: a literature review},
          author = {Chee, C.-H. and Jaafar, J. and Aziz, I. A. and Hasan, M. H. and Yeoh, W.},
            issn = {02692821},
        abstract = {Data Analytics plays an important role in the decision making process. Insights from such pattern analysis offer vast benefits, including increased revenue, cost cutting, and improved competitive advantage. However, the hidden patterns of the frequent itemsets become more time consuming to be mined when the amount of data increases over the time. Moreover, significant memory consumption is needed in mining the hidden patterns of the frequent itemsets due to a heavy computation by the algorithm. Therefore, an efficient algorithm is required to mine the hidden patterns of the frequent itemsets within a shorter run time and with less memory consumption while the volume of data increases over the time period. This paper reviews and presents a comparison of different algorithms for Frequent Pattern Mining (FPM) so that a more efficient FPM algorithm can be developed. {\^A}{\copyright} 2018, The Author(s).},
             url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85044354882&doi=10.1007\%2fs10462-018-9629-z&partnerID=40&md5=d5dab1484f453370f2c1f95dc2aadade},
        keywords = {Competition; Decision making, Competitive advantage; Data analytics; Decision making process; Frequent itemset mining; Frequent pattern mining; Literature reviews; Memory consumption; Pattern analysis, Data mining}
}