Efficient High Utility Itemset Mining Using Genetic Algorithms and Bit-Vector Optimization

Simple item page
dc.contributor.authorAlmeida, Tracy
dc.contributor.authorKhan, Salman
dc.date.accessioned2026-05-05T09:58:48Z
dc.date.available2026-05-05T09:58:48Z
dc.date.issued2024
dc.description.abstractThe paper focuses on High-Utility Itemset Mining (HUIM). The algorithms to mine high utility itemsets face exponential search time due to the growing number of transactions. An algorithm that employs genetic approach has been proposed in this paper to address this challenge. It utilizes techniques of genetic approach to avoid unfit individuals. Transaction clustering is also applied to the database, reducing the time required for database scanning during itemset utility calculations.
dc.identifier.citationAlmeida e Aguiar, T., Khan, S., & Naik, S. B. (2023, November). Efficient High Utility Itemset Mining Using Genetic Algorithms and Bit-Vector Optimization. In International Conference on Data Science, Computation and Security (pp. 369-376). Singapore: Springer Nature Singapore.
dc.identifier.urihttp://rcca.ndl.gov.in/handle/123456789/549
dc.language.isoen
dc.publisherData Science and Security
dc.titleEfficient High Utility Itemset Mining Using Genetic Algorithms and Bit-Vector Optimization
dc.typeArticle
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Salman Efficient High Utility Itemset Mining Using Genetic Algorithms and Bit-Vector Optimization _ SpringerLink.pdf
Size:
446.45 KB
Format:
Adobe Portable Document Format
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download
Collections
Department of BCA