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Title: Incremental itemset mining based on matrix Apriori algorithm
Authors: Oğuz, Damla
Ergenç, Belgin
Keywords: Incremental itemset mining
Matrix Apriori
Learning algorithms
Issue Date: 2012
Publisher: Springer Verlag
Abstract: Databases are updated continuously with increments and re-running the frequent itemset mining algorithms with every update is inefficient. Studies addressing incremental update problem generally propose incremental itemset mining methods based on Apriori and FP-Growth algorithms. Besides inheriting the disadvantages of base algorithms, incremental itemset mining has challenges such as handling i) increments without re-running the algorithm, ii) support changes, iii) new items and iv) addition/deletions in increments. In this paper, we focus on the solution of incremental update problem by proposing the Incremental Matrix Apriori Algorithm. It scans only new transactions, allows the change of minimum support and handles new items in the increments. The base algorithm Matrix Apriori works without candidate generation, scans database only twice and brings additional advantages. Performance studies show that Incremental Matrix Apriori provides speed-up between 41% and 92% while increment size is varied between 5% and 100%.
Description: 14th International Conference on Data Warehousing and Knowledge Discovery, DaWaK 2012; Vienna; Austria; 3 September 2012 through 6 September 2012
ISBN: 978-364232583-0
Appears in Collections:Computer Engineering / Bilgisayar Mühendisliği
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection

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