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Association Rule Mining technique is used to discover the interesting association or correlation among a large set of data items. It plays an important role in generating frequent itemsets from large databases. The Association Rule Mining algorithms such as Apriori, FP-Growth requires repeated scans over the entire database. All the input/output overheads that are being generated during repeated scanning the entire database decrease the performance of CPU, memory and I/O overheads. In this paper, the authors have proposed An Effectual Generalized Mesh Transposition Algorithm (EGMTA) for frequent itemsets generation.
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