Welcome to the InfoSci Platform
Reference Hub4
Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm

Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm

Mehmet Bicer, Daniel Indictor, Ryan Yang, Xiaowen Zhang
Copyright: © 2021 |Volume: 11 |Issue: 1 |Pages: 20
ISSN: 1947-9573|EISSN: 1947-9581|EISBN13: 9781799861409|DOI: 10.4018/IJAL.2021010102
Cite Article Cite Article

MLA

Bicer, Mehmet, et al. "Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm." IJAL vol.11, no.1 2021: pp.18-37. http://doi.org/10.4018/IJAL.2021010102

APA

Bicer, M., Indictor, D., Yang, R., & Zhang, X. (2021). Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm. International Journal of Applied Logistics (IJAL), 11(1), 18-37. http://doi.org/10.4018/IJAL.2021010102

Chicago

Bicer, Mehmet, et al. "Efficient Implementations for UWEP Incremental Frequent Itemset Mining Algorithm," International Journal of Applied Logistics (IJAL) 11, no.1: 18-37. http://doi.org/10.4018/IJAL.2021010102

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Association rule mining is a common technique used in discovering interesting frequent patterns in data acquired in various application domains. The search space combinatorically explodes as the size of the data increases. Furthermore, the introduction of new data can invalidate old frequent patterns and introduce new ones. Hence, while finding the association rules efficiently is an important problem, maintaining and updating them is also crucial. Several algorithms have been introduced to find the association rules efficiently. One of them is Apriori. There are also algorithms written to update or maintain the existing association rules. Update with early pruning (UWEP) is one such algorithm. In this paper, the authors propose that in certain conditions it is preferable to use an incremental algorithm as opposed to the classic Apriori algorithm. They also propose new implementation techniques and improvements to the original UWEP paper in an algorithm we call UWEP2. These include the use of memorization and lazy evaluation to reduce scans of the dataset.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.