Modified bees swarm optimization algorithm for association rules mining

Other Title(s)

خوارزمية سرب النحل الأمثل المعدلة للتنقيب عن قواعد الارتباط

Joint Authors

Duaimi, Mahdi Kazzar
Muhammad, Rasha Abbud
al-Ubaydi, Ahmad Tariq Sadiq

Source

Iraqi Journal of Science

Issue

Vol. 58, Issue 1B (31 Mar. 2017), pp.364-376, 13 p.

Publisher

University of Baghdad College of Science

Publication Date

2017-03-31

Country of Publication

Iraq

No. of Pages

13

Main Subjects

Mathematics

Abstract EN

Mining association rules is a popular and well-studied method of data mining tasks whose primary aim is the discovers of the correlation among sets of items in the transactional databases.

However, generating high- quality association rules in a reasonable time from a given database has been considered as an important and challenging problem, especially with the fast increasing in database's size.

Many algorithms for association rules mining have been already proposed with promosing results.

In this paper, a new association rules mining algorithm based on Bees Swarm Optimization metaheuristic named Modified Bees Swarm Optimization for Association Rules Mining (MBSO-ARM) algorithm is proposed.

Results show that the proposed algorithm can be used as an alternative to the traditional methods.

American Psychological Association (APA)

Muhammad, Rasha Abbud& Duaimi, Mahdi Kazzar& al-Ubaydi, Ahmad Tariq Sadiq. 2017. Modified bees swarm optimization algorithm for association rules mining. Iraqi Journal of Science،Vol. 58, no. 1B, pp.364-376.
https://search.emarefa.net/detail/BIM-732201

Modern Language Association (MLA)

Duaimi, Mahdi Kazzar…[et al.]. Modified bees swarm optimization algorithm for association rules mining. Iraqi Journal of Science Vol. 58, no. 1B (2017), pp.364-376.
https://search.emarefa.net/detail/BIM-732201

American Medical Association (AMA)

Muhammad, Rasha Abbud& Duaimi, Mahdi Kazzar& al-Ubaydi, Ahmad Tariq Sadiq. Modified bees swarm optimization algorithm for association rules mining. Iraqi Journal of Science. 2017. Vol. 58, no. 1B, pp.364-376.
https://search.emarefa.net/detail/BIM-732201

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references : p. 375-376

Record ID

BIM-732201