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DMA: Matrix Based Dynamic Itemset Mining Algorithm
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Author(s): Damla Oguz (Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey), Baris Yildiz (Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey & Department of Computer Engineering, Ege University, Izmir, Turkey)and Belgin Ergenc (Department of Computer Engineering, Izmir Institute of Technology, Izmir, Turkey & Department of Computer Engineering, Dokuz Eylul University, Izmir, Turkey)
Copyright: 2013
Volume: 9
Issue: 4
Pages: 14
Source title:
International Journal of Data Warehousing and Mining (IJDWM)
Editor(s)-in-Chief: Eric Pardede (La Trobe University, Australia)and Kiki Adhinugraha (La Trobe University, Australia)
DOI: 10.4018/ijdwm.2013100104
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Abstract
Updates on an operational database bring forth the challenge of keeping the frequent itemsets up-to-date without re-running the itemset mining algorithms. Studies on dynamic itemset mining, which is the solution to such an update problem, have to address some challenges as handling i) updates without re-running the base algorithm, ii) changes in the support threshold, iii) new items and iv) additions/deletions in updates. The study in this paper is the extension of the Incremental Matrix Apriori Algorithm which proposes solutions to the first three challenges besides inheriting the advantages of the base algorithm which works without candidate generation. In the authors' current work, the authors have improved a former algorithm as to handle updates that are composed of additions and deletions. The authors have also carried out a detailed performance evaluation study on a real and two benchmark datasets.
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