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Comparison of dynamic itemset mining algorithms for multiple support thresholds

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Conference Paper (595.1Kb)
Date
2017-07
Author
Abuzayed, Nourhan
Ergenç, Belgin
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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.
URI
http://doi.org/10.1145/3105831.3105846
http://hdl.handle.net/11147/6460
Collections
  • Computer Engineering / Bilgisayar Mühendisliği [191]
  • Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection [3276]


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