Incremental Algorithm for Association Rule Mining under Dynamic Threshold

Iyad Aqra, Norjihan Abdul Ghani, Carsten Maple, Jose Machado, Nader Sohrabi Safa

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)
10 Downloads (Pure)


Data mining is essentially applied to discover new knowledge from a database through an iterative process. The mining process may be time consuming for massive datasets. A widely used method related to knowledge discovery domain refers to association rule mining (ARM) approach, despite its shortcomings in mining large databases. As such, several approaches have been prescribed to unravel knowledge. Most of the proposed algorithms addressed data incremental issues, especially when a hefty amount of data are added to the database after the latest mining process. Three basic manipulation operations performed in a database include add, delete, and update. Any method devised in light of data incremental issues is bound to embed these three operations. The changing threshold is a long-standing problem within the data mining field. Since decision making refers to an active process, the threshold is indeed changeable. Accordingly, the present study proposes an algorithm that resolves the issue of rescanning a database that had been mined previously and allows retrieval of knowledge that satisfies several thresholds without the need to learn the process from scratch. The proposed approach displayed high accuracy in experimentation, as well as reduction in processing time by almost two-thirds of the original mining execution time.
Original languageEnglish
Article number5398
Number of pages22
JournalApplied Sciences
Issue number24
Publication statusPublished - 10 Dec 2019

Bibliographical note

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (


  • data mining
  • knowledge extraction
  • association rule mining
  • incremental mining
  • dynamic threshold

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