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Title: Comparison of dynamic itemset mining algorithms for multiple support thresholds
Authors: Abuzayed, Nourhan
Ergenç, Belgin
Keywords: Association rule mining
Dynamic itemset mining
Itemset mining
Multiple support thresholds
Data mining
Publisher: Association for Computing Machinery (ACM)
Source: Abuzayed, N., and Ergenç, B. (2017, July 12-14). Comparison of dynamic itemset mining algorithms for multiple support thresholds. Paper presented at the 21st International Database Engineering and Applications Symposium. doi:10.1145/3105831.3105846
Abstract: Mining1 frequent itemsets is an important part of association rule mining process. Handling dynamic aspect of databases and multiple support threshold requirements of items are two important challenges of frequent itemset mining algorithms. Most of the existing dynamic itemset mining algorithms are devised for single support threshold whereas multiple support threshold algorithms are static. This work focuses on dynamic update problem of frequent itemsets under multiple support thresholds and proposes tree-based Dynamic CFP-Growth++ algorithm. Proposed algorithm is compared to our previous dynamic algorithm Dynamic MIS [50] and a recent static algorithm CFP-Growth++ [2] and, findings are; in dynamic database, 1) both of the dynamic algorithms are better than the static algorithm CFP-Growth++, 2) as memory usage performance; Dynamic CFP-Growth++ performs better than Dynamic MIS, 3) as execution time performance; Dynamic MIS is better than Dynamic CFP-Growth++. In short, Dynamic CFP-Growth++ and Dynamic MIS have a trade-off relationship in terms of memory usage and execution time.
Description: 21st International Database Engineering and Applications Symposium, IDEAS 2017; Bristol; United Kingdom; 12 July 2017 through 14 July 2017
ISBN: 9781450352208
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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