Abstract
A prominent subfield of data mining is Frequent Itemset Mining which explores mysterious and hidden patterns in the transaction database. However, as the volume of data increases, the mining of hidden patterns of the frequent itemset is more time-consuming. Moreover, dominant memory consumption is required in mining where the hidden pattern of the frequent itemset computation is complicated through the algorithm. Therefore, a powerful algorithm is needed to mine the hidden patterns of the frequent itemset within a more precise execution time and with lower consumption of memory while the size of data increases over the period. This study article focuses on the pros and cons of FP-growth, LP-growth, FIU-tree, IFP-growth algorithm for frequent pattern discovery, and more efficient frequent pattern mining algorithms can be further carried out.
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Sinthuja, M., Evangeline, D., Raja, S.P., Shanmugarathinam, G. (2022). Frequent Itemset Mining Algorithms—A Literature Survey. In: Raj, J.S., Palanisamy, R., Perikos, I., Shi, Y. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 213. Springer, Singapore. https://doi.org/10.1007/978-981-16-2422-3_13
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