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Incremental algorithm for association rule mining under dynamic threshold
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Aqra, Iyad, Abdul Ghani, Norjihan, Maple, Carsten, Machado, José and Sohrabi Safa, Nader (2019) Incremental algorithm for association rule mining under dynamic threshold. Applied Sciences, 9 (24). 5398. doi:10.3390/app9245398 ISSN 2076-3417.
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Official URL: http://dx.doi.org/10.3390/app9245398
Abstract
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.
| Item Type: | Journal Article | ||||||
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| Subjects: | Q Science > QA Mathematics | ||||||
| Divisions: | Faculty of Science, Engineering and Medicine > Engineering > WMG (Formerly the Warwick Manufacturing Group) | ||||||
| Library of Congress Subject Headings (LCSH): | Data mining, Association rule mining, Computer algorithms, Data sets | ||||||
| Journal or Publication Title: | Applied Sciences | ||||||
| Publisher: | MDPI | ||||||
| ISSN: | 2076-3417 | ||||||
| Official Date: | 10 December 2019 | ||||||
| Dates: |
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| Volume: | 9 | ||||||
| Number: | 24 | ||||||
| Article Number: | 5398 | ||||||
| DOI: | 10.3390/app9245398 | ||||||
| Status: | Peer Reviewed | ||||||
| Publication Status: | Published | ||||||
| Access rights to Published version: | Open Access (Creative Commons) | ||||||
| Date of first compliant deposit: | 13 December 2019 | ||||||
| Date of first compliant Open Access: | 13 December 2019 | ||||||
| RIOXX Funder/Project Grant: |
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