Abstract
Frequent pattern mining (FPM) is one of the most important areas in the field of data mining. Several FPM algorithms have been proposed in the literature by many researchers. In most of the approaches, data set is scanned repeatedly in almost every steps of the algorithm that leads to high time complexity. That is why, processing huge amount of data using those algorithms may not be a suitable option. Hence, a novel FPM algorithm is proposed in this paper that improves efficiency by decreasing the time complexity as compared to classical frequent pattern mining algorithm. The proposed FPM algorithm converts the real-world data set into a binary matrix in a single scan, then join operation is performed to obtain the candidate itemsets. Further, AND operation is performed on the candidates to obtain frequent itemsets. Further more, using our proposed algorithm, interesting association rules can be derived.
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Patro, P.P., Senapati, R. (2021). Advanced Binary Matrix-Based Frequent Pattern Mining Algorithm. In: Udgata, S.K., Sethi, S., Srirama, S.N. (eds) Intelligent Systems. Lecture Notes in Networks and Systems, vol 185. Springer, Singapore. https://doi.org/10.1007/978-981-33-6081-5_27
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DOI: https://doi.org/10.1007/978-981-33-6081-5_27
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