Abstract
In this paper, we propose an efficient algorithm which generates frequent itemsets by only one database scan. A frequent itemset is a set of common items that are included in at least as many transactions as a given minimum support. While scanning the database of transactions, our algorithm generates a table having 1-frequent items and a list of transactions per each 1-frequent item, and generates 2-frequent itemsets by using a hash technique. k(k≥3)-frequent itemsets can be simply found by checking whether for all (k–1)-frequent itemsets used to generate a k-candidate itemset, the number of common transactions in their lists is greater than or equal to the minimum support. The experimental analysis of our algorithm has shown that it can generate frequent itemsets more efficiently than FP-growth algorithm.
This work was supported by Institute of Information Assessment(ITRC).
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Chai, D., Choi, H., Hwang, B. (2005). Discovering Frequent Itemsets Using Transaction Identifiers. In: Wang, L., Jin, Y. (eds) Fuzzy Systems and Knowledge Discovery. FSKD 2005. Lecture Notes in Computer Science(), vol 3613. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539506_147
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DOI: https://doi.org/10.1007/11539506_147
Publisher Name: Springer, Berlin, Heidelberg
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