Abstract
Frequent Pattern Mining (FPM) has been playing an essential role in data mining research. In literature, many algorithms have proposed to discover interesting association patterns. However, frequent pattern mining in a large-scale dataset is a complex task because of a prohibitively large number of the smaller pattern need to be generated first to identify the extended pattern. The complexity in terms of computational time is a big concern. In this paper, we have proposed an updated novel FPM algorithm that uses a boolean matrix and then decomposed the matrix vertically. Here we have reduced the computational time by reducing a few steps and added itemset if and only if satisfy the condition instead of pruning after added into the candidate itemset. Our proposed algorithm has reduced the computational time at least two times on the generation of the boolean matrix (i.e. Q), frequent-1 matrix (i.e., F1) and frequent-1 itemset (i.e., L1), which has improved the efficiency of the algorithm.
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Roy, S., Akhtar, M.N., Rahman, M. (2021). An Improved Boolean Load Matrix-Based Frequent Pattern Mining. In: Vasant, P., Zelinka, I., Weber, GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham. https://doi.org/10.1007/978-3-030-68154-8_86
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DOI: https://doi.org/10.1007/978-3-030-68154-8_86
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